# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import ( MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TextClassificationPipeline, pipeline, ) from transformers.testing_utils import ( is_pipeline_test, is_torch_available, nested_simplify, require_torch, require_torch_bf16, require_torch_fp16, slow, torch_device, ) from .test_pipelines_common import ANY if is_torch_available(): import torch # These 2 model types require different inputs than those of the usual text models. _TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"} @is_pipeline_test class TextClassificationPipelineTests(unittest.TestCase): model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING if not hasattr(model_mapping, "is_dummy"): model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP} @require_torch def test_small_model_pt(self): text_classifier = pipeline(task="text-classification", model="hf-internal-testing/tiny-random-distilbert") outputs = text_classifier("This is great !") self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}]) outputs = text_classifier("This is great !", top_k=2) self.assertEqual( nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}] ) outputs = text_classifier(["This is great !", "This is bad"], top_k=2) self.assertEqual( nested_simplify(outputs), [ [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}], [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}], ], ) outputs = text_classifier("This is great !", top_k=1) self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}]) # Legacy behavior outputs = text_classifier("This is great !", return_all_scores=False) self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}]) outputs = text_classifier("This is great !", return_all_scores=True) self.assertEqual( nested_simplify(outputs), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]] ) outputs = text_classifier(["This is great !", "Something else"], return_all_scores=True) self.assertEqual( nested_simplify(outputs), [ [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}], [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}], ], ) outputs = text_classifier(["This is great !", "Something else"], return_all_scores=False) self.assertEqual( nested_simplify(outputs), [ {"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_0", "score": 0.504}, ], ) # Do not apply any function to output for regression tasks # hack: changing problem_type artificially (so keep this test at last) text_classifier.model.config.problem_type = "regression" outputs = text_classifier("This is great !") self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.01}]) @require_torch def test_accepts_torch_device(self): text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", device=torch_device, ) outputs = text_classifier("This is great !") self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}]) @require_torch_fp16 def test_accepts_torch_fp16(self): text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", device=torch_device, dtype=torch.float16, ) outputs = text_classifier("This is great !") self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}]) @require_torch_bf16 def test_accepts_torch_bf16(self): text_classifier = pipeline( task="text-classification", model="hf-internal-testing/tiny-random-distilbert", device=torch_device, dtype=torch.bfloat16, ) outputs = text_classifier("This is great !") self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}]) @slow @require_torch def test_pt_bert(self): text_classifier = pipeline("text-classification") outputs = text_classifier("This is great !") self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}]) outputs = text_classifier("This is bad !") self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}]) outputs = text_classifier("Birds are a type of animal") self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}]) def get_test_pipeline( self, model, tokenizer=None, image_processor=None, feature_extractor=None, processor=None, dtype="float32", ): text_classifier = TextClassificationPipeline( model=model, tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor, processor=processor, dtype=dtype, ) return text_classifier, ["HuggingFace is in", "This is another test"] def run_pipeline_test(self, text_classifier, _): model = text_classifier.model # Small inputs because BartTokenizer tiny has maximum position embeddings = 22 valid_inputs = "HuggingFace is in" outputs = text_classifier(valid_inputs) self.assertEqual(nested_simplify(outputs), [{"label": ANY(str), "score": ANY(float)}]) self.assertTrue(outputs[0]["label"] in model.config.id2label.values()) valid_inputs = ["HuggingFace is in ", "Paris is in France"] outputs = text_classifier(valid_inputs) self.assertEqual( nested_simplify(outputs), [{"label": ANY(str), "score": ANY(float)}, {"label": ANY(str), "score": ANY(float)}], ) self.assertTrue(outputs[0]["label"] in model.config.id2label.values()) self.assertTrue(outputs[1]["label"] in model.config.id2label.values()) # Forcing to get all results with `top_k=None` # This is NOT the legacy format outputs = text_classifier(valid_inputs, top_k=None) N = len(model.config.id2label.values()) self.assertEqual( nested_simplify(outputs), [[{"label": ANY(str), "score": ANY(float)}] * N, [{"label": ANY(str), "score": ANY(float)}] * N], ) valid_inputs = {"text": "HuggingFace is in ", "text_pair": "Paris is in France"} outputs = text_classifier(valid_inputs) self.assertEqual( nested_simplify(outputs), {"label": ANY(str), "score": ANY(float)}, ) self.assertTrue(outputs["label"] in model.config.id2label.values()) # This might be used a text pair, but tokenizer + pipe interaction # makes it hard to understand that it's not using the pair properly # https://github.com/huggingface/transformers/issues/17305 # We disabled this usage instead as it was outputting wrong outputs. invalid_input = [["HuggingFace is in ", "Paris is in France"]] with self.assertRaises(ValueError): text_classifier(invalid_input) # This used to be valid for doing text pairs # We're keeping it working because of backward compatibility outputs = text_classifier([[["HuggingFace is in ", "Paris is in France"]]]) self.assertEqual( nested_simplify(outputs), [{"label": ANY(str), "score": ANY(float)}], ) self.assertTrue(outputs[0]["label"] in model.config.id2label.values())