Description
Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. xlmr-large-qa-sv
is a Swedish model originally trained by m3hrdadfi
.
How to use
document_assembler = MultiDocumentAssembler() \
.setInputCols(["question", "context"]) \
.setOutputCols(["document_question", "document_context"])
spanClassifier = XlmRoBertaForQuestionAnswering.pretrained("xlm_roberta_qa_xlmr_large_qa_sv_sv_m3hrdadfi","sv") \
.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)
val document = new MultiDocumentAssembler()
.setInputCols(Array("question", "context"))
.setOutputCols(Array("document_question", "document_context"))
val spanClassifier = XlmRoBertaForQuestionAnswering
.pretrained("xlm_roberta_qa_xlmr_large_qa_sv_sv_m3hrdadfi","sv")
.setInputCols(Array("document_question", "document_context"))
.setOutputCol("answer")
.setCaseSensitive(true)
.setMaxSentenceLength(512)
val pipeline = new Pipeline().setStages(Array(document, spanClassifier))
val example = Seq(
("Where was John Lenon born?", "John Lenon was born in London and lived in Paris. My name is Sarah and I live in London."),
("What's my name?", "My name is Clara and I live in Berkeley."))
.toDF("question", "context")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("sv.answer_question.xlmr_roberta.large").predict("""What's my name?|||"My name is Clara and I live in Berkeley.""")
Model Information
Model Name: | xlm_roberta_qa_xlmr_large_qa_sv_sv_m3hrdadfi |
Compatibility: | Spark NLP 4.0.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [question, context] |
Output Labels: | [answer] |
Language: | sv |
Size: | 1.9 GB |
Case sensitive: | true |
Max sentence length: | 512 |
References
- https://huggingface.co/m3hrdadfi/xlmr-large-qa-sv