English ElectraForQuestionAnswering Large model (from mrm8488)

Description

Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. electra-large-finetuned-squadv1 is a English model originally trained by mrm8488.

Download Copy S3 URI

How to use

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

spanClassifier = BertForQuestionAnswering.pretrained("electra_qa_large_finetuned_squadv1","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 = BertForQuestionAnswering.pretrained("electra_qa_large_finetuned_squadv1","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.squad.electra.large.by_mrm8488").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""")

Model Information

Model Name: electra_qa_large_finetuned_squadv1
Compatibility: Spark NLP 4.0.0+
License: Open Source
Edition: Official
Input Labels: [document_question, document_context]
Output Labels: [answer]
Language: en
Size: 1.3 GB
Case sensitive: true
Max sentence length: 512

References

  • https://huggingface.co/mrm8488/electra-large-finetuned-squadv1