Description
Pretrained BertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.bert_qa_mbert_finetuned_mlqa_english_hindi_dev is a Multilingual model originally trained by roshnir.
How to use
document_assembler = MultiDocumentAssembler() \
.setInputCol(["question", "context"]) \
.setOutputCol(["document_question", "document_context"])
spanClassifier = BertForQuestionAnswering.pretrained("bert_qa_mbert_finetuned_mlqa_english_hindi_dev","xx") \
.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 = BertForQuestionAnswering
.pretrained("bert_qa_mbert_finetuned_mlqa_english_hindi_dev", "xx")
.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: | bert_qa_mbert_finetuned_mlqa_english_hindi_dev |
| Compatibility: | Spark NLP 5.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document_question, document_context] |
| Output Labels: | [answer] |
| Language: | xx |
| Size: | 625.5 MB |
References
https://huggingface.co/roshnir/mBert-finetuned-mlqa-dev-en-hi