93 lines
3.9 KiB
Python
93 lines
3.9 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
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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@is_pipeline_test
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class TextClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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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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@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="Narsil/tiny-distilbert-sequence-classification", 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_1", "score": 0.502}])
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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="Narsil/tiny-distilbert-sequence-classification", 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_1", "score": 0.502}])
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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 run_pipeline_test(self, model, tokenizer, feature_extractor):
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text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
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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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