197 lines
8.3 KiB
Python
197 lines
8.3 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import (
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TextClassificationPipeline,
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pipeline,
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)
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from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow, torch_device
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from .test_pipelines_common import ANY
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# These 2 model types require different inputs than those of the usual text models.
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_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
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@is_pipeline_test
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class TextClassificationPipelineTests(unittest.TestCase):
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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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if model_mapping is not None:
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model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
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if tf_model_mapping is not None:
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tf_model_mapping = {
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config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
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}
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@require_torch
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def test_small_model_pt(self):
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text_classifier = pipeline(
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task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
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)
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
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outputs = text_classifier("This is great !", top_k=2)
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self.assertEqual(
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nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]
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)
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outputs = text_classifier(["This is great !", "This is bad"], top_k=2)
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self.assertEqual(
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nested_simplify(outputs),
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[
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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],
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)
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outputs = text_classifier("This is great !", top_k=1)
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
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# Legacy behavior
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outputs = text_classifier("This is great !", return_all_scores=False)
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
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outputs = text_classifier("This is great !", return_all_scores=True)
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self.assertEqual(
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nested_simplify(outputs), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]
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)
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outputs = text_classifier(["This is great !", "Something else"], return_all_scores=True)
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self.assertEqual(
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nested_simplify(outputs),
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[
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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],
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)
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outputs = text_classifier(["This is great !", "Something else"], return_all_scores=False)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{"label": "LABEL_0", "score": 0.504},
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{"label": "LABEL_0", "score": 0.504},
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],
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)
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@require_torch
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def test_accepts_torch_device(self):
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text_classifier = pipeline(
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task="text-classification",
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model="hf-internal-testing/tiny-random-distilbert",
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framework="pt",
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device=torch_device,
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)
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
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@require_tf
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def test_small_model_tf(self):
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text_classifier = pipeline(
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task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf"
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)
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
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@slow
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@require_torch
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def test_pt_bert(self):
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text_classifier = pipeline("text-classification")
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
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outputs = text_classifier("This is bad !")
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self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
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outputs = text_classifier("Birds are a type of animal")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
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@slow
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@require_tf
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def test_tf_bert(self):
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text_classifier = pipeline("text-classification", framework="tf")
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
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outputs = text_classifier("This is bad !")
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self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
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outputs = text_classifier("Birds are a type of animal")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
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def get_test_pipeline(self, model, tokenizer, processor):
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text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
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return text_classifier, ["HuggingFace is in", "This is another test"]
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def run_pipeline_test(self, text_classifier, _):
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model = text_classifier.model
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# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
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valid_inputs = "HuggingFace is in"
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outputs = text_classifier(valid_inputs)
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self.assertEqual(nested_simplify(outputs), [{"label": ANY(str), "score": ANY(float)}])
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self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
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valid_inputs = ["HuggingFace is in ", "Paris is in France"]
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outputs = text_classifier(valid_inputs)
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self.assertEqual(
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nested_simplify(outputs),
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[{"label": ANY(str), "score": ANY(float)}, {"label": ANY(str), "score": ANY(float)}],
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)
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self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
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self.assertTrue(outputs[1]["label"] in model.config.id2label.values())
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# Forcing to get all results with `top_k=None`
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# This is NOT the legacy format
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outputs = text_classifier(valid_inputs, top_k=None)
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N = len(model.config.id2label.values())
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self.assertEqual(
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nested_simplify(outputs),
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[[{"label": ANY(str), "score": ANY(float)}] * N, [{"label": ANY(str), "score": ANY(float)}] * N],
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)
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valid_inputs = {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
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outputs = text_classifier(valid_inputs)
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self.assertEqual(
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nested_simplify(outputs),
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{"label": ANY(str), "score": ANY(float)},
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)
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self.assertTrue(outputs["label"] in model.config.id2label.values())
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# This might be used a text pair, but tokenizer + pipe interaction
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# makes it hard to understand that it's not using the pair properly
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# https://github.com/huggingface/transformers/issues/17305
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# We disabled this usage instead as it was outputting wrong outputs.
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invalid_input = [["HuggingFace is in ", "Paris is in France"]]
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with self.assertRaises(ValueError):
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text_classifier(invalid_input)
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# This used to be valid for doing text pairs
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# We're keeping it working because of backward compatibility
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outputs = text_classifier([[["HuggingFace is in ", "Paris is in France"]]])
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self.assertEqual(
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nested_simplify(outputs),
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[{"label": ANY(str), "score": ANY(float)}],
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)
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self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
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