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Handle long answer needs to be updated. (#14279) 

`start_` and `end_` tensors now contain a batch_size at this point.
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Nicolas Patry 2021-11-06 15:04:30 +01:00 committed by GitHub
parent 843c326ee1
commit 24b30d4d2f
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2 changed files with 7 additions and 1 deletions
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2
src/transformers/pipelines/question_answering.py
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@ -412,7 +412,7 @@ class QuestionAnsweringPipeline(Pipeline):
end_ = np.exp(end_ - np.log(np.sum(np.exp(end_), axis=-1, keepdims=True)))
if handle_impossible_answer:
min_null_score = min(min_null_score, (start_[0] * end_[0]).item())
min_null_score = min(min_null_score, (start_[0, 0] * end_[0, 0]).item())
# Mask CLS
start_[0, 0] = end_[0, 0] = 0.0

6
tests/test_pipelines_question_answering.py
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@ -50,6 +50,12 @@ class QAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
question="Where was HuggingFace founded ?", context="HuggingFace was founded in Paris."
)
self.assertEqual(outputs, {"answer": ANY(str), "start": ANY(int), "end": ANY(int), "score": ANY(float)})
outputs = question_answerer(
question="Where was HuggingFace founded ?",
context="HuggingFace was founded in Paris.",
handle_impossible_answer=True,
)
self.assertEqual(outputs, {"answer": ANY(str), "start": ANY(int), "end": ANY(int), "score": ANY(float)})
outputs = question_answerer(
question=["In what field is HuggingFace working ?", "In what field is HuggingFace working ?"],