2024-09-01-trained_polish_en

docs/_posts/ahmedlone127/2024-09-01-012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_en.md

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+---
+layout: model
+title: English 012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000 DeBertaForSequenceClassification from diogopaes10
+author: John Snow Labs
+name: 012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000
+date: 2024-09-01
+tags: [en, open_source, onnx, sequence_classification, deberta]
+task: Text Classification
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: DeBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained DeBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000` is a English model originally trained by diogopaes10.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_en_5.5.0_3.0_1725210154954.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_en_5.5.0_3.0_1725210154954.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+documentAssembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequenceClassifier  = DeBertaForSequenceClassification.pretrained("012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000","en") \
+     .setInputCols(["documents","token"]) \
+     .setOutputCol("class")
+
+pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+val documentAssembler = new DocumentAssembler()
+    .setInputCols("text")
+    .setOutputCols("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val sequenceClassifier = DeBertaForSequenceClassification.pretrained("012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000", "en")
+    .setInputCols(Array("documents","token")) 
+    .setOutputCol("class") 
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
+val data = Seq("I love spark-nlp").toDS.toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|615.4 MB|
+
+## References
+
+https://huggingface.co/diogopaes10/012-microsoft-deberta-v3-base-finetuned-yahoo-8000_2000

docs/_posts/ahmedlone127/2024-09-01-012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline_en.md

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+---
+layout: model
+title: English 012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline pipeline DeBertaForSequenceClassification from diogopaes10
+author: John Snow Labs
+name: 012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline
+date: 2024-09-01
+tags: [en, open_source, pipeline, onnx]
+task: Text Classification
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained DeBertaForSequenceClassification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline` is a English model originally trained by diogopaes10.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline_en_5.5.0_3.0_1725210205867.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline_en_5.5.0_3.0_1725210205867.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|012_microsoft_deberta_v3_base_finetuned_yahoo_8000_2000_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|615.4 MB|
+
+## References
+
+https://huggingface.co/diogopaes10/012-microsoft-deberta-v3-base-finetuned-yahoo-8000_2000
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- DeBertaForSequenceClassification

docs/_posts/ahmedlone127/2024-09-01-abert_test_en.md

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+---
+layout: model
+title: English abert_test AlbertEmbeddings from hizella
+author: John Snow Labs
+name: abert_test
+date: 2024-09-01
+tags: [en, open_source, onnx, embeddings, albert]
+task: Embeddings
+language: en
+edition: Spark NLP 5.4.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: AlbertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained AlbertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abert_test` is a English model originally trained by hizella.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abert_test_en_5.4.2_3.0_1725205737924.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abert_test_en_5.4.2_3.0_1725205737924.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("document")
+
+tokenizer = Tokenizer() \ 
+      .setInputCols("document") \ 
+      .setOutputCol("token")
+
+embeddings = AlbertEmbeddings.pretrained("abert_test","en") \
+      .setInputCols(["document", "token"]) \
+      .setOutputCol("embeddings")       
+
+pipeline = Pipeline().setStages([documentAssembler, tokenizer, embeddings])
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+val documentAssembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = AlbertEmbeddings.pretrained("abert_test","en") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+val data = Seq("I love spark-nlp").toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|abert_test|
+|Compatibility:|Spark NLP 5.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[albert]|
+|Language:|en|
+|Size:|41.6 MB|
+
+## References
+
+https://huggingface.co/hizella/aBERT_test

docs/_posts/ahmedlone127/2024-09-01-abert_test_pipeline_en.md

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+---
+layout: model
+title: English abert_test_pipeline pipeline AlbertEmbeddings from hizella
+author: John Snow Labs
+name: abert_test_pipeline
+date: 2024-09-01
+tags: [en, open_source, pipeline, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.4.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained AlbertEmbeddings, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`abert_test_pipeline` is a English model originally trained by hizella.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/abert_test_pipeline_en_5.4.2_3.0_1725205740253.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/abert_test_pipeline_en_5.4.2_3.0_1725205740253.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+pipeline = PretrainedPipeline("abert_test_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("abert_test_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|abert_test_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|41.6 MB|
+
+## References
+
+https://huggingface.co/hizella/aBERT_test
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- AlbertEmbeddings