Description
Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. squad-en-de-es-vi-zh-model is a English model originally trained by ZYW.
How to use
documentAssembler = MultiDocumentAssembler() \
.setInputCols(["question", "context"]) \
.setOutputCols(["document_question", "document_context"])
spanClassifier = DistilBertForQuestionAnswering.pretrained("distilbert_qa_squad_en_de_es_vi_zh_model","xx") \
.setInputCols(["document_question", "document_context"]) \
.setOutputCol("answer")\
.setCaseSensitive(True)
pipeline = Pipeline(stages=[documentAssembler, spanClassifier])
data = spark.createDataFrame([["PUT YOUR QUESTION HERE", "PUT YOUR CONTEXT HERE"]]).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 = DistilBertForQuestionAnswering.pretrained("distilbert_qa_squad_en_de_es_vi_zh_model","xx")
.setInputCols(Array("document", "token"))
.setOutputCol("answer")
.setCaseSensitive(true)
val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier))
val data = Seq("PUT YOUR QUESTION HERE", "PUT YOUR CONTEXT HERE").toDF("question", "context")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("xx.answer_question.squad.distil_bert._en_de_es_vi_zh_tuned.by_ZYW").predict("""PUT YOUR QUESTION HERE|||"PUT YOUR CONTEXT HERE""")
Model Information
| Model Name: | distilbert_qa_squad_en_de_es_vi_zh_model |
| Compatibility: | Spark NLP 4.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document_question, document_context] |
| Output Labels: | [answer] |
| Language: | xx |
| Size: | 505.7 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |
References
- https://huggingface.co/ZYW/squad-en-de-es-vi-zh-model