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  • John Snow Labs
  • Sep 11, 2024

German roberta_legal_german_cased_german_legal_squad_part_augmented_2000 RoBertaForQuestionAnswering from farid1088

  • de
  • open_source
  • onnx
  • question_answering
  • roberta

Description

Pretrained RoBertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.roberta_legal_german_cased_german_legal_squad_part_augmented_2000 is a German model originally trained by farid1088.

Download Copy S3 URI

How to use

             
documentAssembler = MultiDocumentAssembler() \
     .setInputCol(["question", "context"]) \
     .setOutputCol(["document_question", "document_context"])
    
spanClassifier = RoBertaForQuestionAnswering.pretrained("roberta_legal_german_cased_german_legal_squad_part_augmented_2000","de") \
     .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("roberta_legal_german_cased_german_legal_squad_part_augmented_2000", "de")
    .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: roberta_legal_german_cased_german_legal_squad_part_augmented_2000
Compatibility: Spark NLP 5.5.0+
License: Open Source
Edition: Official
Input Labels: [document_question, document_context]
Output Labels: [answer]
Language: de
Size: 465.8 MB

References

https://huggingface.co/farid1088/RoBERTa-legal-de-cased_German_legal_SQuAD_part_augmented_2000