# 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 import numpy as np from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline from transformers.pipelines import AggregationStrategy, Pipeline, TokenClassificationArgumentHandler from transformers.testing_utils import nested_simplify, require_tf, require_torch, slow from .test_pipelines_common import CustomInputPipelineCommonMixin VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]] class TokenClassificationPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase): pipeline_task = "ner" small_models = [ "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english" ] # Default model - Models tested without the @slow decorator large_models = [] # Models tested with the @slow decorator def _test_pipeline(self, token_classifier: Pipeline): output_keys = {"entity", "word", "score", "start", "end", "index"} if token_classifier.aggregation_strategy != AggregationStrategy.NONE: output_keys = {"entity_group", "word", "score", "start", "end"} self.assertIsNotNone(token_classifier) mono_result = token_classifier(VALID_INPUTS[0]) self.assertIsInstance(mono_result, list) self.assertIsInstance(mono_result[0], (dict, list)) if isinstance(mono_result[0], list): mono_result = mono_result[0] for key in output_keys: self.assertIn(key, mono_result[0]) multi_result = [token_classifier(input) for input in VALID_INPUTS] self.assertIsInstance(multi_result, list) self.assertIsInstance(multi_result[0], (dict, list)) if isinstance(multi_result[0], list): multi_result = multi_result[0] for result in multi_result: for key in output_keys: self.assertIn(key, result) @require_torch @slow def test_spanish_bert(self): # https://github.com/huggingface/transformers/pull/4987 NER_MODEL = "mrm8488/bert-spanish-cased-finetuned-ner" model = AutoModelForTokenClassification.from_pretrained(NER_MODEL) tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True) sentence = """Consuelo Araújo Noguera, ministra de cultura del presidente Andrés Pastrana (1998.2002) fue asesinada por las Farc luego de haber permanecido secuestrada por algunos meses.""" token_classifier = pipeline("ner", model=model, tokenizer=tokenizer) output = token_classifier(sentence) self.assertEqual( nested_simplify(output[:3]), [ {"entity": "B-PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4, "index": 1}, {"entity": "B-PER", "score": 0.803, "word": "##uelo", "start": 4, "end": 8, "index": 2}, {"entity": "I-PER", "score": 0.999, "word": "Ara", "start": 9, "end": 12, "index": 3}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") output = token_classifier(sentence) self.assertEqual( nested_simplify(output[:3]), [ {"entity_group": "PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4}, {"entity_group": "PER", "score": 0.966, "word": "##uelo Araújo Noguera", "start": 4, "end": 23}, {"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first") output = token_classifier(sentence) self.assertEqual( nested_simplify(output[:3]), [ {"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23}, {"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75}, {"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max") output = token_classifier(sentence) self.assertEqual( nested_simplify(output[:3]), [ {"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23}, {"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75}, {"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average") output = token_classifier(sentence) self.assertEqual( nested_simplify(output[:3]), [ {"entity_group": "PER", "score": 0.966, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23}, {"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75}, {"entity_group": "ORG", "score": 0.542, "word": "Farc", "start": 110, "end": 114}, ], ) @require_torch @slow def test_dbmdz_english(self): # Other sentence NER_MODEL = "dbmdz/bert-large-cased-finetuned-conll03-english" model = AutoModelForTokenClassification.from_pretrained(NER_MODEL) tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True) sentence = """Enzo works at the the UN""" token_classifier = pipeline("ner", model=model, tokenizer=tokenizer) output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity": "I-PER", "score": 0.997, "word": "En", "start": 0, "end": 2, "index": 1}, {"entity": "I-PER", "score": 0.996, "word": "##zo", "start": 2, "end": 4, "index": 2}, {"entity": "I-ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24, "index": 7}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first") output = token_classifier(sentence) self.assertEqual( nested_simplify(output[:3]), [ {"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max") output = token_classifier(sentence) self.assertEqual( nested_simplify(output[:3]), [ {"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average") output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24}, ], ) @require_torch def test_aggregation_strategy(self): model_name = self.small_models[0] tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt") # Just to understand scores indexes in this test self.assertEqual( token_classifier.model.config.id2label, {0: "O", 1: "B-MISC", 2: "I-MISC", 3: "B-PER", 4: "I-PER", 5: "B-ORG", 6: "I-ORG", 7: "B-LOC", 8: "I-LOC"}, ) example = [ { # fmt : off "scores": np.array([0, 0, 0, 0, 0.9968166351318359, 0, 0, 0]), "index": 1, "is_subword": False, "word": "En", "start": 0, "end": 2, }, { # fmt : off "scores": np.array([0, 0, 0, 0, 0.9957635998725891, 0, 0, 0]), "index": 2, "is_subword": True, "word": "##zo", "start": 2, "end": 4, }, { # fmt: off "scores": np.array([0, 0, 0, 0, 0, 0.9986497163772583, 0, 0, ]), # fmt: on "index": 7, "word": "UN", "is_subword": False, "start": 11, "end": 13, }, ] self.assertEqual( nested_simplify(token_classifier.aggregate(example, AggregationStrategy.NONE)), [ {"end": 2, "entity": "I-PER", "score": 0.997, "start": 0, "word": "En", "index": 1}, {"end": 4, "entity": "I-PER", "score": 0.996, "start": 2, "word": "##zo", "index": 2}, {"end": 13, "entity": "B-ORG", "score": 0.999, "start": 11, "word": "UN", "index": 7}, ], ) self.assertEqual( nested_simplify(token_classifier.aggregate(example, AggregationStrategy.SIMPLE)), [ {"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13}, ], ) self.assertEqual( nested_simplify(token_classifier.aggregate(example, AggregationStrategy.FIRST)), [ {"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13}, ], ) self.assertEqual( nested_simplify(token_classifier.aggregate(example, AggregationStrategy.MAX)), [ {"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13}, ], ) self.assertEqual( nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)), [ {"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13}, ], ) @require_torch def test_aggregation_strategy_example2(self): model_name = self.small_models[0] tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt") # Just to understand scores indexes in this test self.assertEqual( token_classifier.model.config.id2label, {0: "O", 1: "B-MISC", 2: "I-MISC", 3: "B-PER", 4: "I-PER", 5: "B-ORG", 6: "I-ORG", 7: "B-LOC", 8: "I-LOC"}, ) example = [ { # Necessary for AVERAGE "scores": np.array([0, 0.55, 0, 0.45, 0, 0, 0, 0, 0, 0]), "is_subword": False, "index": 1, "word": "Ra", "start": 0, "end": 2, }, { "scores": np.array([0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0]), "is_subword": True, "word": "##ma", "start": 2, "end": 4, "index": 2, }, { # 4th score will have the higher average # 4th score is B-PER for this model # It's does not correspond to any of the subtokens. "scores": np.array([0, 0, 0, 0.4, 0, 0, 0.6, 0, 0, 0]), "is_subword": True, "word": "##zotti", "start": 11, "end": 13, "index": 3, }, ] self.assertEqual( token_classifier.aggregate(example, AggregationStrategy.NONE), [ {"end": 2, "entity": "B-MISC", "score": 0.55, "start": 0, "word": "Ra", "index": 1}, {"end": 4, "entity": "B-LOC", "score": 0.8, "start": 2, "word": "##ma", "index": 2}, {"end": 13, "entity": "I-ORG", "score": 0.6, "start": 11, "word": "##zotti", "index": 3}, ], ) self.assertEqual( token_classifier.aggregate(example, AggregationStrategy.FIRST), [{"entity_group": "MISC", "score": 0.55, "word": "Ramazotti", "start": 0, "end": 13}], ) self.assertEqual( token_classifier.aggregate(example, AggregationStrategy.MAX), [{"entity_group": "LOC", "score": 0.8, "word": "Ramazotti", "start": 0, "end": 13}], ) self.assertEqual( nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)), [{"entity_group": "PER", "score": 0.35, "word": "Ramazotti", "start": 0, "end": 13}], ) @require_torch def test_gather_pre_entities(self): model_name = self.small_models[0] tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt") sentence = "Hello there" tokens = tokenizer( sentence, return_attention_mask=False, return_tensors="pt", truncation=True, return_special_tokens_mask=True, return_offsets_mapping=True, ) offset_mapping = tokens.pop("offset_mapping").cpu().numpy()[0] special_tokens_mask = tokens.pop("special_tokens_mask").cpu().numpy()[0] input_ids = tokens["input_ids"].numpy()[0] # First element in [CLS] scores = np.array([[1, 0, 0], [0.1, 0.3, 0.6], [0.8, 0.1, 0.1]]) pre_entities = token_classifier.gather_pre_entities( sentence, input_ids, scores, offset_mapping, special_tokens_mask ) self.assertEqual( nested_simplify(pre_entities), [ {"word": "Hello", "scores": [0.1, 0.3, 0.6], "start": 0, "end": 5, "is_subword": False, "index": 1}, { "word": "there", "scores": [0.8, 0.1, 0.1], "index": 2, "start": 6, "end": 11, "is_subword": False, }, ], ) @require_tf def test_tf_only(self): model_name = "Narsil/small" # This model only has a TensorFlow version # We test that if we don't specificy framework='tf', it gets detected automatically token_classifier = pipeline(task="ner", model=model_name) self._test_pipeline(token_classifier) @require_tf def test_tf_defaults(self): for model_name in self.small_models: tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="tf") self._test_pipeline(token_classifier) @require_tf def test_tf_small_ignore_subwords_available_for_fast_tokenizers(self): for model_name in self.small_models: tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline( task="ner", model=model_name, tokenizer=tokenizer, framework="tf", aggregation_strategy=AggregationStrategy.FIRST, ) self._test_pipeline(token_classifier) for model_name in self.small_models: tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline( task="ner", model=model_name, tokenizer=tokenizer, framework="tf", aggregation_strategy=AggregationStrategy.SIMPLE, ) self._test_pipeline(token_classifier) @require_torch def test_pt_ignore_subwords_slow_tokenizer_raises(self): model_name = self.small_models[0] tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) with self.assertRaises(ValueError): pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.FIRST) with self.assertRaises(ValueError): pipeline( task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.AVERAGE ) with self.assertRaises(ValueError): pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.MAX) @require_torch def test_pt_defaults_slow_tokenizer(self): for model_name in self.small_models: tokenizer = AutoTokenizer.from_pretrained(model_name) token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer) self._test_pipeline(token_classifier) @require_torch def test_pt_defaults(self): for model_name in self.small_models: token_classifier = pipeline(task="ner", model=model_name) self._test_pipeline(token_classifier) @slow @require_torch def test_warnings(self): with self.assertWarns(UserWarning): token_classifier = pipeline(task="ner", model=self.small_models[0], grouped_entities=True) self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.SIMPLE) with self.assertWarns(UserWarning): token_classifier = pipeline( task="ner", model=self.small_models[0], grouped_entities=True, ignore_subwords=True ) self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.FIRST) @slow @require_torch def test_simple(self): token_classifier = pipeline(task="ner", model="dslim/bert-base-NER", grouped_entities=True) sentence = "Hello Sarah Jessica Parker who Jessica lives in New York" sentence2 = "This is a simple test" output = token_classifier(sentence) output_ = nested_simplify(output) self.assertEqual( output_, [ { "entity_group": "PER", "score": 0.996, "word": "Sarah Jessica Parker", "start": 6, "end": 26, }, {"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38}, {"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56}, ], ) output = token_classifier([sentence, sentence2]) output_ = nested_simplify(output) self.assertEqual( output_, [ [ {"entity_group": "PER", "score": 0.996, "word": "Sarah Jessica Parker", "start": 6, "end": 26}, {"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38}, {"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56}, ], [], ], ) @require_torch def test_pt_small_ignore_subwords_available_for_fast_tokenizers(self): for model_name in self.small_models: tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline( task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True ) self._test_pipeline(token_classifier) for model_name in self.small_models: tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) token_classifier = pipeline( task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=False ) self._test_pipeline(token_classifier) class TokenClassificationArgumentHandlerTestCase(unittest.TestCase): def setUp(self): self.args_parser = TokenClassificationArgumentHandler() def test_simple(self): string = "This is a simple input" inputs, offset_mapping = self.args_parser(string) self.assertEqual(inputs, [string]) self.assertEqual(offset_mapping, None) inputs, offset_mapping = self.args_parser([string, string]) self.assertEqual(inputs, [string, string]) self.assertEqual(offset_mapping, None) inputs, offset_mapping = self.args_parser(string, offset_mapping=[(0, 1), (1, 2)]) self.assertEqual(inputs, [string]) self.assertEqual(offset_mapping, [[(0, 1), (1, 2)]]) inputs, offset_mapping = self.args_parser( [string, string], offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]] ) self.assertEqual(inputs, [string, string]) self.assertEqual(offset_mapping, [[(0, 1), (1, 2)], [(0, 2), (2, 3)]]) def test_errors(self): string = "This is a simple input" # 2 sentences, 1 offset_mapping, args with self.assertRaises(TypeError): self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]]) # 2 sentences, 1 offset_mapping, args with self.assertRaises(TypeError): self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)]) # 2 sentences, 1 offset_mapping, input_list with self.assertRaises(ValueError): self.args_parser([string, string], offset_mapping=[[(0, 1), (1, 2)]]) # 2 sentences, 1 offset_mapping, input_list with self.assertRaises(ValueError): self.args_parser([string, string], offset_mapping=[(0, 1), (1, 2)]) # 1 sentences, 2 offset_mapping with self.assertRaises(ValueError): self.args_parser(string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]]) # 0 sentences, 1 offset_mapping with self.assertRaises(TypeError): self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])