Description
Financial RoBerta-based Question Answering model, trained on squad-v2, finetuned on proprietary Financial questions and answers.
Predicted Entities
How to use
documentAssembler = nlp.MultiDocumentAssembler()\
.setInputCols(["question", "context"])\
.setOutputCols(["document_question", "document_context"])
spanClassifier = nlp.RoBertaForQuestionAnswering.pretrained("finqa_roberta","en", "finance/models") \
.setInputCols(["document_question", "document_context"]) \
.setOutputCol("answer") \
.setCaseSensitive(True)
pipeline = nlp.Pipeline(stages=[documentAssembler, spanClassifier])
example = spark.createDataFrame([["What is the current total Operating Profit?", "Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004"]]).toDF("question", "context")
result = pipeline.fit(example).transform(example)
result.select('answer.result').show()
Results
`9.4 mn , down from EUR 11.7`
Model Information
| Model Name: | finqa_roberta |
| Compatibility: | Finance NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [question, context] |
| Output Labels: | [answer] |
| Language: | en |
| Size: | 248.1 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |
References
Trained on squad-v2, finetuned on proprietary Financial questions and answers.