2024-09-19-ner_chunkyun_pipeline_en

docs/_posts/ahmedlone127/2024-09-02-koobert_pipeline_xx.md

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+---
+layout: model
+title: Multilingual koobert_pipeline pipeline BertEmbeddings from KooAI
+author: John Snow Labs
+name: koobert_pipeline
+date: 2024-09-02
+tags: [xx, open_source, pipeline, onnx]
+task: Embeddings
+language: xx
+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 BertEmbeddings, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`koobert_pipeline` is a Multilingual model originally trained by KooAI.
+
+{:.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/koobert_pipeline_xx_5.5.0_3.0_1725318896545.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/koobert_pipeline_xx_5.5.0_3.0_1725318896545.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("koobert_pipeline", lang = "xx")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("koobert_pipeline", lang = "xx")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|koobert_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|xx|
+|Size:|689.1 MB|
+
+## References
+
+https://huggingface.co/KooAI/KooBERT
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- BertEmbeddings

docs/_posts/ahmedlone127/2024-09-02-tweets_sentiment_model_60k_samples_pipeline_en.md

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+---
+layout: model
+title: English tweets_sentiment_model_60k_samples_pipeline pipeline DistilBertForSequenceClassification from ivanscorral
+author: John Snow Labs
+name: tweets_sentiment_model_60k_samples_pipeline
+date: 2024-09-02
+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 DistilBertForSequenceClassification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`tweets_sentiment_model_60k_samples_pipeline` is a English model originally trained by ivanscorral.
+
+{:.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/tweets_sentiment_model_60k_samples_pipeline_en_5.5.0_3.0_1725305885755.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/tweets_sentiment_model_60k_samples_pipeline_en_5.5.0_3.0_1725305885755.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("tweets_sentiment_model_60k_samples_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("tweets_sentiment_model_60k_samples_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|tweets_sentiment_model_60k_samples_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|249.5 MB|
+
+## References
+
+https://huggingface.co/ivanscorral/tweets-sentiment-model-60k-samples
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- DistilBertForSequenceClassification

docs/_posts/ahmedlone127/2024-09-05-bert_full_finetuned_ner_pablo_pipeline_en.md

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+---
+layout: model
+title: English bert_full_finetuned_ner_pablo_pipeline pipeline BertForTokenClassification from pabRomero
+author: John Snow Labs
+name: bert_full_finetuned_ner_pablo_pipeline
+date: 2024-09-05
+tags: [en, open_source, pipeline, onnx]
+task: Named Entity Recognition
+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 BertForTokenClassification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_full_finetuned_ner_pablo_pipeline` is a English model originally trained by pabRomero.
+
+{:.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/bert_full_finetuned_ner_pablo_pipeline_en_5.5.0_3.0_1725538599406.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_full_finetuned_ner_pablo_pipeline_en_5.5.0_3.0_1725538599406.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("bert_full_finetuned_ner_pablo_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("bert_full_finetuned_ner_pablo_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_full_finetuned_ner_pablo_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|407.3 MB|
+
+## References
+
+https://huggingface.co/pabRomero/BERT-full-finetuned-ner-pablo
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- BertForTokenClassification

docs/_posts/ahmedlone127/2024-09-05-secdisclosure_28l_en.md

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+---
+layout: model
+title: English secdisclosure_28l RoBertaForSequenceClassification from EGAPE
+author: John Snow Labs
+name: secdisclosure_28l
+date: 2024-09-05
+tags: [en, open_source, onnx, sequence_classification, roberta]
+task: Text Classification
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: RoBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained RoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`secdisclosure_28l` is a English model originally trained by EGAPE.
+
+{:.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/secdisclosure_28l_en_5.5.0_3.0_1725541670119.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/secdisclosure_28l_en_5.5.0_3.0_1725541670119.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  = RoBertaForSequenceClassification.pretrained("secdisclosure_28l","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 = RoBertaForSequenceClassification.pretrained("secdisclosure_28l", "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:|secdisclosure_28l|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|309.7 MB|
+
+## References
+
+https://huggingface.co/EGAPE/secdisclosure-28l