English RobertaForQuestionAnswering (from AmazonScience)

Description

Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. qanlu is a English model originally trained by AmazonScience.

Download Copy S3 URI

How to use

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

spanClassifier = RoBertaForQuestionAnswering.pretrained("roberta_qa_qanlu","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)
val document = new MultiDocumentAssembler()
.setInputCols("question", "context")
.setOutputCols("document_question", "document_context")

val spanClassifier = RoBertaForQuestionAnswering
.pretrained("roberta_qa_qanlu","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)
import nlu
nlu.load("en.answer_question.roberta.by_AmazonScience").predict("""What's my name?|||"My name is Clara and I live in Berkeley.""")

Model Information

Model Name: roberta_qa_qanlu
Compatibility: Spark NLP 4.0.0+
License: Open Source
Edition: Official
Input Labels: [question, context]
Output Labels: [answer]
Language: en
Size: 464.1 MB
Case sensitive: true
Max sentence length: 512

References

  • https://huggingface.co/AmazonScience/qanlu
  • https://github.com/amazon-research/question-answering-nlu
  • https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf