Description
Pretrained RoBertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.babyberta_wikipedia1_2_5_with_masking_run2_finetuned_qasrl is a English model originally trained by lielbin.
How to use
documentAssembler = MultiDocumentAssembler() \
.setInputCol(["question", "context"]) \
.setOutputCol(["document_question", "document_context"])
spanClassifier = RoBertaForQuestionAnswering.pretrained("babyberta_wikipedia1_2_5_with_masking_run2_finetuned_qasrl","en") \
.setInputCols(["document_question","document_context"]) \
.setOutputCol("answer")
pipeline = Pipeline().setStages([documentAssembler, spanClassifier])
data = spark.createDataFrame([["What framework do I use?","I use spark-nlp."]]).toDF("document_question", "document_context")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val documentAssembler = new MultiDocumentAssembler()
.setInputCol(Array("question", "context"))
.setOutputCol(Array("document_question", "document_context"))
val spanClassifier = RoBertaForQuestionAnswering.pretrained("babyberta_wikipedia1_2_5_with_masking_run2_finetuned_qasrl", "en")
.setInputCols(Array("document_question","document_context"))
.setOutputCol("answer")
val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier))
val data = Seq("What framework do I use?","I use spark-nlp.").toDS.toDF("document_question", "document_context")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | babyberta_wikipedia1_2_5_with_masking_run2_finetuned_qasrl |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document_question, document_context] |
| Output Labels: | [answer] |
| Language: | en |
| Size: | 32.0 MB |
References
https://huggingface.co/lielbin/BabyBERTa-wikipedia1_2.5-with-Masking_run2-finetuned-QASRL