Portuguese BertForQuestionAnswering model (from pierreguillou)

Description

Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-large-cased-squad-v1.1-portuguese is a Portuguese model orginally trained by pierreguillou.

Download Copy S3 URI

How to use

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

spanClassifier = BertForQuestionAnswering.pretrained("bert_qa_bert_large_cased_squad_v1.1_portuguese","pt") \
.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)
val document = new MultiDocumentAssembler()
.setInputCols("question", "context")
.setOutputCols("document_question", "document_context")

val spanClassifier = BertForQuestionAnswering
.pretrained("bert_qa_bert_large_cased_squad_v1.1_portuguese","pt")
.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)
import nlu
nlu.load("pt.answer_question.squad.bert.large_cased").predict("""What's my name?|||"My name is Clara and I live in Berkeley.""")

Model Information

Model Name: bert_qa_bert_large_cased_squad_v1.1_portuguese
Compatibility: Spark NLP 4.0.0+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [embeddings]
Language: pt
Size: 1.2 GB
Case sensitive: true
Max sentence length: 512

References

  • https://huggingface.co/pierreguillou/bert-large-cased-squad-v1.1-portuguese
  • https://github.com/piegu/language-models/blob/master/question_answering_BERT_large_cased_squad_v11_pt.ipynb
  • https://nbviewer.jupyter.org/github/piegu/language-models/blob/master/question_answering_BERT_large_cased_squad_v11_pt.ipynb
  • https://medium.com/@pierre_guillou/nlp-como-treinar-um-modelo-de-question-answering-em-qualquer-linguagem-baseado-no-bert-large-1c899262dd96#c2f5
  • https://ailab.unb.br/
  • https://www.linkedin.com/in/pierreguillou/
  • http://www.deeplearningbrasil.com.br/
  • https://neuralmind.ai/
  • https://medium.com/@pierre_guillou/nlp-como-treinar-um-modelo-de-question-answering-em-qualquer-linguagem-baseado-no-bert-large-1c899262dd96