Description
Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-swedish-cased-squad-experimental
is a Swedish model originally trained by KBLab
.
Predicted Entities
How to use
documentAssembler = MultiDocumentAssembler() \
.setInputCols(["question", "context"]) \
.setOutputCols(["document_question", "document_context"])
spanClassifier = BertForQuestionAnswering.pretrained("bert_qa_base_swedish_cased_squad_experimental","sv") \
.setInputCols(["document_question", "document_context"]) \
.setOutputCol("answer")\
.setCaseSensitive(True)
pipeline = Pipeline(stages=[documentAssembler, spanClassifier])
data = spark.createDataFrame([["Vad är mitt namn?", "Jag heter Clara och jag bor i Berkeley."]]).toDF("question", "context")
result = pipeline.fit(data).transform(data)
val documentAssembler = new MultiDocumentAssembler()
.setInputCols(Array("question", "context"))
.setOutputCols(Array("document_question", "document_context"))
val spanClassifer = BertForQuestionAnswering.pretrained("bert_qa_base_swedish_cased_squad_experimental","sv")
.setInputCols(Array("document", "token"))
.setOutputCol("answer")
.setCaseSensitive(true)
val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier))
val data = Seq("Vad är mitt namn?", "Jag heter Clara och jag bor i Berkeley.").toDF("question", "context")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("sv.answer_question.bert.squad.cased_base.by_KBLab").predict("""Vad är mitt namn?|||"Jag heter Clara och jag bor i Berkeley.""")
Model Information
Model Name: | bert_qa_base_swedish_cased_squad_experimental |
Compatibility: | Spark NLP 5.2.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document_question, document_context] |
Output Labels: | [answer] |
Language: | sv |
Size: | 465.2 MB |
Case sensitive: | true |
Max sentence length: | 512 |
References
References
- https://huggingface.co/KBLab/bert-base-swedish-cased-squad-experimental