Description
Pretrained RobertaForQuestionAnswering 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.
How to use
Document_Assembler = MultiDocumentAssembler()\
.setInputCols(["question", "context"])\
.setOutputCols(["document_question", "document_context"])
Question_Answering = RoBertaForQuestionAnswering.pretrained("roberta_qa_nlu","en")\
.setInputCols(["document_question", "document_context"])\
.setOutputCol("answer")\
.setCaseSensitive(True)
pipeline = Pipeline(stages=[Document_Assembler, Question_Answering])
data = spark.createDataFrame([["What's my name?","My name is Clara and I live in Berkeley."]]).toDF("question", "context")
result = pipeline.fit(data).transform(data)
val Document_Assembler = new MultiDocumentAssembler()
.setInputCols(Array("question", "context"))
.setOutputCols(Array("document_question", "document_context"))
val Question_Answering = RoBertaForQuestionAnswering.pretrained("roberta_qa_nlu","en")
.setInputCols(Array("document_question", "document_context"))
.setOutputCol("answer")
.setCaseSensitive(True)
val pipeline = new Pipeline().setStages(Array(Document_Assembler, Question_Answering))
val data = Seq("What's my name?","My name is Clara and I live in Berkeley.").toDS.toDF("question", "context")
val result = pipeline.fit(data).transform(data)
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_nlu |
| Compatibility: | Spark NLP 4.2.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 464.1 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/AmazonScience/qanlu
- https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf
- https://github.com/amazon-research/question-answering-nlu