Description
Pretrained RoBertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.roberta_base_uy22_cased_finetuned_nlquad is a English model originally trained by jmachado.
How to use
document_assembler = MultiDocumentAssembler() \
.setInputCol(["question", "context"]) \
.setOutputCol(["document_question", "document_context"])
spanClassifier = RoBertaForQuestionAnswering.pretrained("roberta_base_uy22_cased_finetuned_nlquad","en") \
.setInputCols(["document_question","document_context"]) \
.setOutputCol("answer")
pipeline = Pipeline().setStages([document_assembler, spanClassifier])
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val document_assembler = new MultiDocumentAssembler()
.setInputCol(Array("question", "context"))
.setOutputCol(Array("document_question", "document_context"))
val spanClassifier = RoBertaForQuestionAnswering
.pretrained("roberta_base_uy22_cased_finetuned_nlquad", "en")
.setInputCols(Array("document_question","document_context"))
.setOutputCol("answer")
val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier))
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | roberta_base_uy22_cased_finetuned_nlquad |
| Compatibility: | Spark NLP 5.2.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document_question, document_context] |
| Output Labels: | [answer] |
| Language: | en |
| Size: | 407.2 MB |
References
https://huggingface.co/jmachado/roberta-base-uy22-cased-finetuned-nlquad