Japanese BertForQuestionAnswering Large model (from KoichiYasuoka)

Description

Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-large-japanese-wikipedia-ud-head is a Japanese model originally trained by KoichiYasuoka.

Download Copy S3 URI

How to use

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

spanClassifier = BertForQuestionAnswering.pretrained("bert_qa_large_japanese_wikipedia_ud_head","ja") \
.setInputCols(["document_question", "document_context"]) \
.setOutputCol("answer")\
.setCaseSensitive(True)

pipeline = Pipeline(stages=[documentAssembler, spanClassifier])

data = spark.createDataFrame([["私の名前は何ですか?", "私の名前はクララで、私はバークレーに住んでいます。"]]).toDF("question", "context")

result = pipeline.fit(data).transform(data)
val documentAssembler = new MultiDocumentAssembler() 
.setInputCols(Array("question", "context")) 
.setOutputCols(Array("document_question", "document_context"))

val spanClassifer = BertForQuestionAnswering.pretrained("bert_qa_large_japanese_wikipedia_ud_head","ja") 
.setInputCols(Array("document", "token")) 
.setOutputCol("answer")
.setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier))

val data = Seq("私の名前は何ですか?", "私の名前はクララで、私はバークレーに住んでいます。").toDF("question", "context")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("ja.answer_question.wikipedia.bert.large").predict("""私の名前は何ですか?|||"私の名前はクララで、私はバークレーに住んでいます。""")

Model Information

Model Name: bert_qa_large_japanese_wikipedia_ud_head
Compatibility: Spark NLP 4.0.0+
License: Open Source
Edition: Official
Input Labels: [document_question, document_context]
Output Labels: [answer]
Language: ja
Size: 1.2 GB
Case sensitive: true
Max sentence length: 512

References

  • https://huggingface.co/KoichiYasuoka/bert-large-japanese-wikipedia-ud-head
  • https://github.com/UniversalDependencies/UD_Japanese-GSDLUW