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  • John Snow Labs
  • Apr 04, 2023

English BertForQuestionAnswering Cased model (from Shobhank-iiitdwd)

  • en
  • open_source
  • bert
  • question_answering
  • tensorflow

Description

Pretrained BertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. BERT-L-QA is a English model originally trained by Shobhank-iiitdwd.

Download Copy S3 URI

How to use

Document_Assembler = MultiDocumentAssembler()\
     .setInputCols(["question", "context"])\
     .setOutputCols(["document_question", "document_context"])

Question_Answering = BertForQuestionAnswering.pretrained("Bert_qa_l","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)

val Document_Assembler = new MultiDocumentAssembler()
     .setInputCols(Array("question", "context"))
     .setOutputCols(Array("document_question", "document_context"))

val Question_Answering = BertForQuestionAnswering.pretrained("Bert_qa_l","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)

Model Information

Model Name: Bert_qa_l
Compatibility: Spark NLP 4.4.0+
License: Open Source
Edition: Official
Input Labels: [document_question, document_context]
Output Labels: [answer]
Language: en
Size: 1.3 GB
Case sensitive: true
Max sentence length: 512

References

  • https://huggingface.co/Shobhank-iiitdwd/BERT-L-QA
  • https://haystack.deepset.ai/tutorials/first-qa-system
  • https://github.com/deepset-ai/haystack/
  • http://deepset.ai/
  • https://haystack.deepset.ai/
  • https://deepset.ai/german-bert
  • https://deepset.ai/germanquad
  • https://github.com/deepset-ai/haystack
  • https://docs.haystack.deepset.ai
  • https://haystack.deepset.ai/community
  • https://twitter.com/deepset_ai
  • https://www.linkedin.com/company/deepset-ai/
  • https://haystack.deepset.ai/community/join
  • https://github.com/deepset-ai/haystack/discussions
  • https://deepset.ai
  • http://www.deepset.ai/jobs
  • https://paperswithcode.com/sota?task=Question+Answering&dataset=squad_v2