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  • John Snow Labs
  • Apr 05, 2023

Portuguese BertForQuestionAnswering Base Cased model (from pierreguillou)

  • pt
  • 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-base-cased-squad-v1.1-portuguese is a Portuguese model originally trained by pierreguillou.

Download Copy S3 URI

How to use

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

Question_Answering = BertForQuestionAnswering.pretrained("Bert_qa_base_cased_squad_v1.1_portuguese","pt")\
     .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_base_cased_squad_v1.1_portuguese","pt")
     .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_base_cased_squad_v1.1_portuguese
Compatibility: Spark NLP 4.4.0+
License: Open Source
Edition: Official
Input Labels: [document_question, document_context]
Output Labels: [answer]
Language: pt
Size: 406.5 MB
Case sensitive: true
Max sentence length: 512

References

  • https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese
  • https://miro.medium.com/max/2000/1*te5MmdesAHCmg4KmK8zD3g.png
  • http://www.deeplearningbrasil.com.br/
  • https://neuralmind.ai/
  • https://medium.com/@pierre_guillou/nlp-modelo-de-question-answering-em-qualquer-idioma-baseado-no-bert-base-estudo-de-caso-em-12093d385e78
  • https://colab.research.google.com/drive/18ueLdi_V321Gz37x4gHq8mb4XZSGWfZx?usp=sharing
  • https://github.com/piegu/language-models/blob/master/colab_question_answering_BERT_base_cased_squad_v11_pt.ipynb
  • https://www.linkedin.com/in/pierreguillou/
  • https://medium.com/@pierre_guillou/nlp-modelo-de-question-answering-em-qualquer-idioma-baseado-no-bert-base-estudo-de-caso-em-12093d385e78#c572
  • https://neuralmind.ai/
  • http://www.deeplearningbrasil.com.br/
  • https://colab.research.google.com/
  • https://ailab.unb.br/