2023-11-12-bert_qa_base_cased_squad2_en

docs/_posts/ahmedlone127/2023-11-12-bert_base_cased_qa_squad2_en.md

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+---
+layout: model
+title: English BertForQuestionAnswering model (from deepset)
+author: John Snow Labs
+name: bert_base_cased_qa_squad2
+date: 2023-11-12
+tags: [en, open_source, question_answering, bert, onnx]
+task: Question Answering
+language: en
+edition: Spark NLP 5.2.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-cased-squad2` is a English model orginally trained by `deepset`.
+
+## 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/bert_base_cased_qa_squad2_en_5.2.0_3.0_1699785841705.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_cased_qa_squad2_en_5.2.0_3.0_1699785841705.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"])
+
+spanClassifier = BertForQuestionAnswering.pretrained("bert_base_cased_qa_squad2","en") \
+.setInputCols(["document_question", "document_context"]) \
+.setOutputCol("answer") \
+.setCaseSensitive(True)
+
+pipeline = Pipeline().setStages([
+document_assembler,
+spanClassifier
+])
+
+example = spark.createDataFrame([["What's my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context")
+
+result = pipeline.fit(example).transform(example)
+```
+```scala
+val document = new MultiDocumentAssembler()
+.setInputCols("question", "context")
+.setOutputCols("document_question", "document_context")
+
+val spanClassifier = BertForQuestionAnswering
+.pretrained("bert_base_cased_qa_squad2","en")
+.setInputCols(Array("document_question", "document_context"))
+.setOutputCol("answer")
+.setCaseSensitive(true)
+.setMaxSentenceLength(512)
+
+val pipeline = new Pipeline().setStages(Array(document, spanClassifier))
+
+val example = Seq(
+("Where was John Lenon born?", "John Lenon was born in London and lived in Paris. My name is Sarah and I live in London."),
+("What's my name?", "My name is Clara and I live in Berkeley."))
+.toDF("question", "context")
+
+val result = pipeline.fit(example).transform(example)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.answer_question.squadv2.bert.base_cased.by_deepset").predict("""What's my name?|||"My name is Clara and I live in Berkeley.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_base_cased_qa_squad2|
+|Compatibility:|Spark NLP 5.2.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|403.6 MB|
+|Case sensitive:|true|
+|Max sentence length:|512|
+
+## References
+
+References
+
+- https://huggingface.co/deepset/bert-base-cased-squad2

docs/_posts/ahmedlone127/2023-11-12-bert_qa_3lang_xx.md

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+---
+layout: model
+title: Multilingual BertForQuestionAnswering Cased model (from krinal214)
+author: John Snow Labs
+name: bert_qa_3lang
+date: 2023-11-12
+tags: [xx, open_source, bert, question_answering, onnx]
+task: Question Answering
+language: xx
+edition: Spark NLP 5.2.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-3lang` is a Multilingual model originally trained by `krinal214`.
+
+## 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/bert_qa_3lang_xx_5.2.0_3.0_1699786324189.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_qa_3lang_xx_5.2.0_3.0_1699786324189.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 = MultiDocumentAssembler() \
+    .setInputCols(["question", "context"]) \
+    .setOutputCols(["document_question", "document_context"])
+
+spanClassifier = BertForQuestionAnswering.pretrained("bert_qa_3lang","xx") \
+    .setInputCols(["document_question", "document_context"]) \
+    .setOutputCol("answer")\
+    .setCaseSensitive(True)
+
+pipeline = Pipeline(stages=[documentAssembler, spanClassifier])
+
+data = spark.createDataFrame([["PUT YOUR QUESTION HERE", "PUT YOUR CONTEXT HERE"]]).toDF("question", "context")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new MultiDocumentAssembler() 
+      .setInputCols(Array("question", "context")) 
+      .setOutputCols(Array("document_question", "document_context"))
+
+val spanClassifer = BertForQuestionAnswering.pretrained("bert_qa_3lang","xx") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("answer")
+    .setCaseSensitive(true)
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier))
+
+val data = Seq("PUT YOUR QUESTION HERE", "PUT YOUR CONTEXT HERE").toDF("question", "context")
+
+val result = pipeline.fit(data).transform(data)
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("xx.answer_question.bert.tydiqa.3lang").predict("""PUT YOUR QUESTION HERE|||"PUT YOUR CONTEXT HERE""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_qa_3lang|
+|Compatibility:|Spark NLP 5.2.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document_question, document_context]|
+|Output Labels:|[answer]|
+|Language:|xx|
+|Size:|665.0 MB|
+|Case sensitive:|true|
+|Max sentence length:|512|
+
+## References
+
+References
+
+- https://huggingface.co/krinal214/bert-3lang

docs/_posts/ahmedlone127/2023-11-12-bert_qa_adars_base_cased_finetuned_squad_en.md

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+---
+layout: model
+title: English BertForQuestionAnswering Base Cased model (from Adars)
+author: John Snow Labs
+name: bert_qa_adars_base_cased_finetuned_squad
+date: 2023-11-12
+tags: [en, open_source, bert, question_answering, onnx]
+task: Question Answering
+language: en
+edition: Spark NLP 5.2.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `bert-base-cased-finetuned-squad` is a English model originally trained by `Adars`.
+
+## 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/bert_qa_adars_base_cased_finetuned_squad_en_5.2.0_3.0_1699788427985.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_qa_adars_base_cased_finetuned_squad_en_5.2.0_3.0_1699788427985.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 = BertForQuestionAnswering.pretrained("bert_qa_adars_base_cased_finetuned_squad","en")\
+     .setInputCols(["document_question", "document_context"])\
+     .setOutputCol("answer")\
+     .setCaseSensitive(True)
+
+pipeline = Pipeline(stages=[Document_Assembler, Question_Answering])
+
+data = spark.createDataFrame([["What's my name?","My name is Clara and I live in Berkeley."]]).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 = BertForQuestionAnswering.pretrained("bert_qa_adars_base_cased_finetuned_squad","en")
+     .setInputCols(Array("document_question", "document_context"))
+     .setOutputCol("answer")
+     .setCaseSensitive(true)
+
+val pipeline = new Pipeline().setStages(Array(Document_Assembler, Question_Answering))
+
+val data = Seq("What's my name?","My name is Clara and I live in Berkeley.").toDS.toDF("question", "context")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_qa_adars_base_cased_finetuned_squad|
+|Compatibility:|Spark NLP 5.2.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document_question, document_context]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|403.7 MB|
+|Case sensitive:|true|
+|Max sentence length:|512|
+
+## References
+
+References
+
+- https://huggingface.co/Adars/bert-base-cased-finetuned-squad