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Fixing #13381 #13400

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merged 2 commits into from Sep 9, 2021
Merged

Fixing #13381 #13400

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c669fc6
Fixing #13381
Narsil Sep 3, 2021
657ec58
Enabling automatic LED models.
Narsil Sep 3, 2021
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34 changes: 26 additions & 8 deletions src/transformers/pipelines/zero_shot_classification.py
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Expand Up @@ -88,7 +88,7 @@ def _parse_and_tokenize(
hypothesis_template,
padding=True,
add_special_tokens=True,
truncation=TruncationStrategy.DO_NOT_TRUNCATE,
truncation=TruncationStrategy.ONLY_FIRST,
**kwargs
):
"""
Expand All @@ -113,13 +113,31 @@ def _parse_and_tokenize(
)
inputs.append(model_input)
else:
inputs = self.tokenizer(
sequence_pairs,
add_special_tokens=add_special_tokens,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
)
try:
inputs = self.tokenizer(
sequence_pairs,
add_special_tokens=add_special_tokens,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
)
except Exception as e:
if "too short" in str(e):
# tokenizers might yell that we want to truncate
# to a value that is not even reached by the input.
# In that case we don't want to truncate.
# It seems there's not a really better way to catch that
# exception.
Comment on lines +125 to +130
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Could you provide a reproducer for this issue? I can't seem to find where that would come from

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Basic test for the pipelines RUN_PIPELINE_TESTS=1 pytest -sv tests/test_pipelines_zero_shot_classification.py will do (on the LED test)


inputs = self.tokenizer(
sequence_pairs,
add_special_tokens=add_special_tokens,
return_tensors=return_tensors,
padding=padding,
truncation=TruncationStrategy.DO_NOT_TRUNCATE,
)
else:
raise e

return inputs

Expand Down
14 changes: 14 additions & 0 deletions tests/test_pipelines_zero_shot.py
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Expand Up @@ -105,6 +105,20 @@ def run_entailment_id(self, zero_shot_classifier: Pipeline):
zero_shot_classifier.model.config.label2id = original_label2id
self.assertEqual(original_entailment, zero_shot_classifier.entailment_id)

@require_torch
def test_truncation(self):
zero_shot_classifier = pipeline(
"zero-shot-classification",
model="sshleifer/tiny-distilbert-base-cased-distilled-squad",
framework="pt",
)
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"Who are you voting for in 2020?" * 100, candidate_labels=["politics", "public health", "science"]
)

@require_torch
def test_small_model_pt(self):
zero_shot_classifier = pipeline(
Expand Down