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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@@ -37,7 +37,7 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
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model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
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tf_model_mapping = TF_MODEL_FOR_TOKEN_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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token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
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return token_classifier, ["A simple string", "A simple string that is quite a bit longer"]
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