English AlbertForQuestionAnswering model (from twmkn9)

Description

Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. albert-base-v2-squad2 is a English model originally trained by twmkn9.

Predicted Entities

Download Copy S3 URI

How to use

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

spanClassifier = AlbertForQuestionAnswering.pretrained("albert_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 = AlbertForQuestionAnswering.pretrained("albert_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.span_question.albert").predict("""What is my name?|||"My name is Clara and I live in Berkeley.""")

Model Information

Model Name: albert_base_qa_squad2
Compatibility: Spark NLP 5.1.2+
License: Open Source
Edition: Official
Input Labels: [document_question, document_context]
Output Labels: [answer]
Language: en
Size: 42.0 MB

References

References

https://huggingface.co/twmkn9/albert-base-v2-squad2