Description
Pretrained Question Answering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1
is a English model orginally trained by madlag
.
How to use
document_assembler = MultiDocumentAssembler() \
.setInputCols(["question", "context"]) \
.setOutputCols(["document_question", "document_context"])
spanClassifier = BertForQuestionAnswering.pretrained("bert_qa_bert_large_uncased_wwm_squadv2_x2.15_f83.2_d25_hybrid_v1","en") \
.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("question", "context")
.setOutputCols("document_question", "document_context")
val spanClassifier = BertForQuestionAnswering
.pretrained("bert_qa_bert_large_uncased_wwm_squadv2_x2.15_f83.2_d25_hybrid_v1","en")
.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("en.answer_question.squadv2.bert.large_uncased_v2_x2.15_f83.2_d25_hybrid.by_madlag").predict("""What's my name?|||"My name is Clara and I live in Berkeley.""")
Model Information
Model Name: | bert_qa_bert_large_uncased_wwm_squadv2_x2.15_f83.2_d25_hybrid_v1 |
Compatibility: | Spark NLP 4.0.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [sentence, token] |
Output Labels: | [embeddings] |
Language: | en |
Size: | 455.4 MB |
Case sensitive: | false |
Max sentence length: | 512 |
References
- https://huggingface.co/madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1
- https://rajpurkar.github.io/SQuAD-explorer
- https://www.aclweb.org/anthology/N19-1423.pdf