Description
Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. mqa-baseline is a English model orginally trained by xraychen.
How to use
document_assembler = MultiDocumentAssembler() \
.setInputCols(["question", "context"]) \
.setOutputCols(["document_question", "document_context"])
spanClassifier = BertForQuestionAnswering.pretrained("bert_qa_mqa_baseline","en") \
.setInputCols(["document_question", "document_context"]) \
.setOutputCol("answer") \
.setCaseSensitive(True)
pipeline = Pipeline().setStages([
document_assembler,
spanClassifier
])
example = spark.createDataFrame([["What's my name?", "My name is Clara and I live in Berkeley."]]).toDF("question", "context")
result = pipeline.fit(example).transform(example)
Model Information
| Model Name: | bert_qa_mqa_baseline |
| Compatibility: | Spark NLP 4.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 407.7 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |
References
- https://huggingface.co/xraychen/mqa-baseline