Description
Pretrained BertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-finnish-cased-squad1-fi is a Finnish model originally trained by ilmariky.
Predicted Entities
How to use
Document_Assembler = MultiDocumentAssembler()\
.setInputCols(["question", "context"])\
.setOutputCols(["document_question", "document_context"])
Question_Answering = BertForQuestionAnswering.pretrained("bert_qa_base_nnish_cased_squad1","fi")\
.setInputCols(["document_question", "document_context"])\
.setOutputCol("answer")\
.setCaseSensitive(True)
pipeline = Pipeline(stages=[Document_Assembler, Question_Answering])
data = spark.createDataFrame([["What's my name?","My name is Clara and I live in Berkeley."]]).toDF("question", "context")
result = pipeline.fit(data).transform(data)
val Document_Assembler = new MultiDocumentAssembler()
.setInputCols(Array("question", "context"))
.setOutputCols(Array("document_question", "document_context"))
val Question_Answering = BertForQuestionAnswering.pretrained("bert_qa_base_nnish_cased_squad1","fi")
.setInputCols(Array("document_question", "document_context"))
.setOutputCol("answer")
.setCaseSensitive(true)
val pipeline = new Pipeline().setStages(Array(Document_Assembler, Question_Answering))
val data = Seq("What's my name?","My name is Clara and I live in Berkeley.").toDS.toDF("question", "context")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | bert_qa_base_nnish_cased_squad1 |
| Compatibility: | Spark NLP 5.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document_question, document_context] |
| Output Labels: | [answer] |
| Language: | fi |
| Size: | 464.7 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |
References
References
- https://huggingface.co/ilmariky/bert-base-finnish-cased-squad1-fi
- https://github.com/google-research-datasets/tydiqa
- https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/