Pipeline testing - using tiny models on Hub (#20426)
* rework pipeline tests * run pipeline tests * fix * fix * fix * revert the changes in get_test_pipeline() parameter list * fix expected error message * skip a test * clean up --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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@@ -30,7 +30,7 @@ class ZeroShotClassificationPipelineTests(unittest.TestCase, metaclass=PipelineT
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model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
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tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
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def get_test_pipeline(self, model, tokenizer, feature_extractor, image_processor):
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def get_test_pipeline(self, model, tokenizer, processor):
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classifier = ZeroShotClassificationPipeline(
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model=model, tokenizer=tokenizer, candidate_labels=["polics", "health"]
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)
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