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- # 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)]])
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