English XlmRoBertaForQuestionAnswering model (from deepset)

Description

Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. xlm-roberta-base-squad2 is a English model originally trained by deepset.

Download Copy S3 URI

How to use

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

spanClassifier = XlmRoBertaForQuestionAnswering.pretrained("xlm_roberta_base_qa_squad2","en") \
.setInputCols(["document_question", "document_context"]) \
.setOutputCol("answer")\
.setCaseSensitive(True)

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

data = spark.createDataFrame([["What is my name?", "My name is Clara and I live in Berkeley."]]).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 = XlmRoBertaForQuestionAnswering.pretrained("xlm_roberta_base_qa_squad2","en") 
.setInputCols(Array("document", "token")) 
.setOutputCol("answer")
.setCaseSensitive(true)

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

val data = Seq("What is my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.answer_question.squadv2.xlm_roberta.base").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""")

Model Information

Model Name: xlm_roberta_base_qa_squad2
Compatibility: Spark NLP 4.0.0+
License: Open Source
Edition: Official
Input Labels: [document_question, document_context]
Output Labels: [answer]
Language: en
Size: 875.0 MB
Case sensitive: false
Max sentence length: 512

References

https://huggingface.co/deepset/xlm-roberta-base-squad2