Description
Pretrained RobertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-base-ca-cased-qa is a Catalan model originally trained by projecte-aina.
How to use
Document_Assembler = MultiDocumentAssembler()\
.setInputCols(["question", "context"])\
.setOutputCols(["document_question", "document_context"])
Question_Answering = RoBertaForQuestionAnswering.pretrained("roberta_qa_base_ca_cased","ca")\
.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 = RoBertaForQuestionAnswering.pretrained("roberta_qa_base_ca_cased","ca")
.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)
import nlu
nlu.load("ca.answer_question.roberta.cased_base").predict("""What's my name?|||"My name is Clara and I live in Berkeley.""")
Model Information
| Model Name: | roberta_qa_base_ca_cased |
| Compatibility: | Spark NLP 4.2.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | ca |
| Size: | 451.1 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/projecte-aina/roberta-base-ca-cased-qa
- https://arxiv.org/abs/1907.11692
- https://github.com/projecte-aina/club
- https://www.apache.org/licenses/LICENSE-2.0
- https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca%7Cen
- https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina