Description
Pretrained BertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-italian-uncased-squad-it
is a Italian model originally trained by antoniocappiello
.
How to use
Document_Assembler = MultiDocumentAssembler()\
.setInputCols(["question", "context"])\
.setOutputCols(["document_question", "document_context"])
Question_Answering = BertForQuestionAnswering.pretrained("Bert_qa_base_alian_uncased_squad","it")\
.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_alian_uncased_squad","it")
.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_alian_uncased_squad |
Compatibility: | Spark NLP 4.4.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document_question, document_context] |
Output Labels: | [answer] |
Language: | it |
Size: | 410.3 MB |
Case sensitive: | false |
Max sentence length: | 512 |
References
- https://huggingface.co/antoniocappiello/bert-base-italian-uncased-squad-it
- http://sag.art.uniroma2.it/demo-software/squadit/
- https://github.com/crux82/squad-it/blob/master/README.md#evaluating-a-neural-model-over-squad-it