2024-08-30-camembert_base_qa_fquad_fr

docs/_posts/ahmedlone127/2024-08-30-camembert_base_qa_fquad_fr.md

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+---
+layout: model
+title: French CamemBertForQuestionAnswering Base squadFR (camembert_base_qa_fquad)
+author: John Snow Labs
+name: camembert_base_qa_fquad
+date: 2024-08-30
+tags: [fr, french, question_answering, camembert, open_source, onnx, openvino]
+task: Question Answering
+language: fr
+edition: Spark NLP 5.4.2
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: BertForZeroShotClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `camembert_base_qa_fquad ` is a French model originally fine-tuned on a combo of three French Q&A datasets:
+
+- PIAFv1.1
+- FQuADv1.0
+- SQuAD-FR (SQuAD automatically translated to French)
+
+## Predicted Entities
+
+
+
+{:.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/camembert_base_qa_fquad_fr_5.4.2_3.0_1725015714535.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/camembert_base_qa_fquad_fr_5.4.2_3.0_1725015714535.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
+Document_Assembler = MultiDocumentAssembler()\
+     .setInputCols(["question", "context"])\
+     .setOutputCols(["document_question", "document_context"])
+
+Question_Answering = CamemBertForQuestionAnswering("camembert_base_qa_fquad","fr")\
+     .setInputCols(["document_question", "document_context"])\
+     .setOutputCol("answer")\
+     .setCaseSensitive(True)
+
+pipeline = Pipeline(stages=[Document_Assembler, Question_Answering])
+
+data = spark.createDataFrame([["Où est-ce que je vis?","Mon nom est Wolfgang et je vis à Berlin."]]).toDF("question", "context")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val Document_Assembler = new MultiDocumentAssembler()
+     .setInputCols(Array("question", "context"))
+     .setOutputCols(Array("document_question", "document_context"))
+
+val Question_Answering = CamemBertForQuestionAnswering("camembert_base_qa_fquad","fr")
+     .setInputCols(Array("document_question", "document_context"))
+     .setOutputCol("answer")
+     .setCaseSensitive(True)
+
+val pipeline = new Pipeline().setStages(Array(Document_Assembler, Question_Answering))
+
+val data = Seq("Où est-ce que je vis?","Mon nom est Wolfgang et je vis à Berlin.").toDS.toDF("question", "context")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("fr.answer_question.camembert.fquad").predict("""Où est-ce que je vis?|||"Mon nom est Wolfgang et je vis à Berlin.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|camembert_base_qa_fquad|
+|Compatibility:|Spark NLP 5.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[label]|
+|Language:|fr|
+|Size:|667.9 MB|
+|Case sensitive:|true|
+
+## References
+
+References
+
+References
+
+https://huggingface.co/etalab-ia/camembert-base-squadFR-fquad-piaf
+
+## Benchmarking
+
+```bash
+
+
+{"f1": 80.61, "exact_match": 59.54}
+```

docs/_posts/ahmedlone127/2024-08-30-combined_flan_t5_xl_classifier_en.md

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+---
+layout: model
+title: English combined_flan_t5_xl_classifier T5Transformer from illuminoplanet
+author: John Snow Labs
+name: combined_flan_t5_xl_classifier
+date: 2024-08-30
+tags: [en, open_source, onnx, t5, question_answering, summarization, translation, text_generation]
+task: [Question Answering, Summarization, Translation, Text Generation]
+language: en
+edition: Spark NLP 5.4.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: T5Transformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained T5Transformer model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`combined_flan_t5_xl_classifier` is a English model originally trained by illuminoplanet.
+
+{:.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/combined_flan_t5_xl_classifier_en_5.4.2_3.0_1725048919360.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/combined_flan_t5_xl_classifier_en_5.4.2_3.0_1725048919360.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')
+
+t5  = T5Transformer.pretrained("combined_flan_t5_xl_classifier","en") \
+     .setInputCols(["document"]) \
+     .setOutputCol("output")
+
+pipeline = Pipeline().setStages([documentAssembler, t5])
+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 t5 = T5Transformer.pretrained("combined_flan_t5_xl_classifier", "en")
+    .setInputCols(Array("documents")) 
+    .setOutputCol("output") 
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))
+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:|combined_flan_t5_xl_classifier|
+|Compatibility:|Spark NLP 5.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document]|
+|Output Labels:|[output]|
+|Language:|en|
+|Size:|2.9 GB|
+
+## References
+
+https://huggingface.co/illuminoplanet/combined_flan_t5_xl_classifier

docs/_posts/ahmedlone127/2024-08-30-combined_flan_t5_xl_classifier_pipeline_en.md

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+---
+layout: model
+title: English combined_flan_t5_xl_classifier_pipeline pipeline T5Transformer from illuminoplanet
+author: John Snow Labs
+name: combined_flan_t5_xl_classifier_pipeline
+date: 2024-08-30
+tags: [en, open_source, pipeline, onnx]
+task: [Question Answering, Summarization, Translation, Text Generation]
+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 T5Transformer, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`combined_flan_t5_xl_classifier_pipeline` is a English model originally trained by illuminoplanet.
+
+{:.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/combined_flan_t5_xl_classifier_pipeline_en_5.4.2_3.0_1725049073632.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/combined_flan_t5_xl_classifier_pipeline_en_5.4.2_3.0_1725049073632.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("combined_flan_t5_xl_classifier_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("combined_flan_t5_xl_classifier_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|combined_flan_t5_xl_classifier_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|2.9 GB|
+
+## References
+
+https://huggingface.co/illuminoplanet/combined_flan_t5_xl_classifier
+
+## Included Models
+
+- DocumentAssembler
+- T5Transformer

docs/_posts/ahmedlone127/2024-08-30-mt5_tsonga_para_en.md

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+---
+layout: model
+title: English mt5_tsonga_para T5Transformer from boneb
+author: John Snow Labs
+name: mt5_tsonga_para
+date: 2024-08-30
+tags: [en, open_source, onnx, t5, question_answering, summarization, translation, text_generation]
+task: [Question Answering, Summarization, Translation, Text Generation]
+language: en
+edition: Spark NLP 5.4.2
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: T5Transformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained T5Transformer model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mt5_tsonga_para` is a English model originally trained by boneb.
+
+{:.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/mt5_tsonga_para_en_5.4.2_3.0_1725052109767.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mt5_tsonga_para_en_5.4.2_3.0_1725052109767.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')
+
+t5  = T5Transformer.pretrained("mt5_tsonga_para","en") \
+     .setInputCols(["document"]) \
+     .setOutputCol("output")
+
+pipeline = Pipeline().setStages([documentAssembler, t5])
+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 t5 = T5Transformer.pretrained("mt5_tsonga_para", "en")
+    .setInputCols(Array("documents")) 
+    .setOutputCol("output") 
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))
+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:|mt5_tsonga_para|
+|Compatibility:|Spark NLP 5.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document]|
+|Output Labels:|[output]|
+|Language:|en|
+|Size:|2.3 GB|
+
+## References
+
+https://huggingface.co/boneb/mt5-TS-para