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fd3b12e8c3
* Fixed: Better names for nlp variables in pipelines' tests and docs. * Fixed: Better variable names
552 lines
23 KiB
Python
552 lines
23 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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import numpy as np
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from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
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from transformers.pipelines import AggregationStrategy, Pipeline, TokenClassificationArgumentHandler
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from transformers.testing_utils import nested_simplify, require_tf, require_torch, slow
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from .test_pipelines_common import CustomInputPipelineCommonMixin
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VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
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class TokenClassificationPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "ner"
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small_models = [
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"sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
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] # Default model - Models tested without the @slow decorator
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large_models = [] # Models tested with the @slow decorator
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def _test_pipeline(self, token_classifier: Pipeline):
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output_keys = {"entity", "word", "score", "start", "end", "index"}
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if token_classifier.aggregation_strategy != AggregationStrategy.NONE:
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output_keys = {"entity_group", "word", "score", "start", "end"}
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self.assertIsNotNone(token_classifier)
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mono_result = token_classifier(VALID_INPUTS[0])
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], (dict, list))
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if isinstance(mono_result[0], list):
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mono_result = mono_result[0]
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for key in output_keys:
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self.assertIn(key, mono_result[0])
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multi_result = [token_classifier(input) for input in VALID_INPUTS]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in output_keys:
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self.assertIn(key, result)
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@require_torch
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@slow
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def test_spanish_bert(self):
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# https://github.com/huggingface/transformers/pull/4987
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NER_MODEL = "mrm8488/bert-spanish-cased-finetuned-ner"
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model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
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tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
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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."""
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer)
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output[:3]),
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[
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{"entity": "B-PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4, "index": 1},
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{"entity": "B-PER", "score": 0.803, "word": "##uelo", "start": 4, "end": 8, "index": 2},
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{"entity": "I-PER", "score": 0.999, "word": "Ara", "start": 9, "end": 12, "index": 3},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output[:3]),
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[
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{"entity_group": "PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4},
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{"entity_group": "PER", "score": 0.966, "word": "##uelo Araújo Noguera", "start": 4, "end": 23},
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{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output[:3]),
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[
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{"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
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{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
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{"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output[:3]),
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[
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{"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
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{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
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{"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output[:3]),
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[
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{"entity_group": "PER", "score": 0.966, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
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{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
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{"entity_group": "ORG", "score": 0.542, "word": "Farc", "start": 110, "end": 114},
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],
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)
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@require_torch
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@slow
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def test_dbmdz_english(self):
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# Other sentence
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NER_MODEL = "dbmdz/bert-large-cased-finetuned-conll03-english"
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model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
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tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
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sentence = """Enzo works at the the UN"""
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer)
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output),
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[
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{"entity": "I-PER", "score": 0.997, "word": "En", "start": 0, "end": 2, "index": 1},
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{"entity": "I-PER", "score": 0.996, "word": "##zo", "start": 2, "end": 4, "index": 2},
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{"entity": "I-ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24, "index": 7},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output),
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[
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{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output[:3]),
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[
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{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output[:3]),
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[
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{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24},
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],
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)
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token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average")
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output = token_classifier(sentence)
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self.assertEqual(
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nested_simplify(output),
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[
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{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 22, "end": 24},
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],
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)
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@require_torch
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def test_aggregation_strategy(self):
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model_name = self.small_models[0]
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
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# Just to understand scores indexes in this test
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self.assertEqual(
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token_classifier.model.config.id2label,
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{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"},
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)
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example = [
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{
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# fmt : off
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"scores": np.array([0, 0, 0, 0, 0.9968166351318359, 0, 0, 0]),
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"index": 1,
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"is_subword": False,
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"word": "En",
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"start": 0,
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"end": 2,
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},
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{
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# fmt : off
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"scores": np.array([0, 0, 0, 0, 0.9957635998725891, 0, 0, 0]),
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"index": 2,
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"is_subword": True,
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"word": "##zo",
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"start": 2,
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"end": 4,
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},
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{
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# fmt: off
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"scores": np.array([0, 0, 0, 0, 0, 0.9986497163772583, 0, 0, ]),
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# fmt: on
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"index": 7,
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"word": "UN",
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"is_subword": False,
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"start": 11,
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"end": 13,
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},
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]
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self.assertEqual(
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nested_simplify(token_classifier.aggregate(example, AggregationStrategy.NONE)),
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[
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{"end": 2, "entity": "I-PER", "score": 0.997, "start": 0, "word": "En", "index": 1},
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{"end": 4, "entity": "I-PER", "score": 0.996, "start": 2, "word": "##zo", "index": 2},
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{"end": 13, "entity": "B-ORG", "score": 0.999, "start": 11, "word": "UN", "index": 7},
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],
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)
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self.assertEqual(
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nested_simplify(token_classifier.aggregate(example, AggregationStrategy.SIMPLE)),
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[
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{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
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],
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)
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self.assertEqual(
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nested_simplify(token_classifier.aggregate(example, AggregationStrategy.FIRST)),
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[
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{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
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],
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)
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self.assertEqual(
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nested_simplify(token_classifier.aggregate(example, AggregationStrategy.MAX)),
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[
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{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
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],
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)
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self.assertEqual(
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nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)),
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[
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{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
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],
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)
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@require_torch
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def test_aggregation_strategy_example2(self):
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model_name = self.small_models[0]
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
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# Just to understand scores indexes in this test
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self.assertEqual(
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token_classifier.model.config.id2label,
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{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"},
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)
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example = [
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{
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# Necessary for AVERAGE
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"scores": np.array([0, 0.55, 0, 0.45, 0, 0, 0, 0, 0, 0]),
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"is_subword": False,
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"index": 1,
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"word": "Ra",
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"start": 0,
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"end": 2,
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},
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{
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"scores": np.array([0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0]),
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"is_subword": True,
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"word": "##ma",
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"start": 2,
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"end": 4,
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"index": 2,
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},
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{
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# 4th score will have the higher average
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# 4th score is B-PER for this model
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# It's does not correspond to any of the subtokens.
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"scores": np.array([0, 0, 0, 0.4, 0, 0, 0.6, 0, 0, 0]),
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"is_subword": True,
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"word": "##zotti",
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"start": 11,
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"end": 13,
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"index": 3,
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},
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]
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self.assertEqual(
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token_classifier.aggregate(example, AggregationStrategy.NONE),
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[
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{"end": 2, "entity": "B-MISC", "score": 0.55, "start": 0, "word": "Ra", "index": 1},
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{"end": 4, "entity": "B-LOC", "score": 0.8, "start": 2, "word": "##ma", "index": 2},
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{"end": 13, "entity": "I-ORG", "score": 0.6, "start": 11, "word": "##zotti", "index": 3},
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],
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)
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self.assertEqual(
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token_classifier.aggregate(example, AggregationStrategy.FIRST),
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[{"entity_group": "MISC", "score": 0.55, "word": "Ramazotti", "start": 0, "end": 13}],
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)
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self.assertEqual(
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token_classifier.aggregate(example, AggregationStrategy.MAX),
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[{"entity_group": "LOC", "score": 0.8, "word": "Ramazotti", "start": 0, "end": 13}],
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)
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self.assertEqual(
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nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)),
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[{"entity_group": "PER", "score": 0.35, "word": "Ramazotti", "start": 0, "end": 13}],
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)
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@require_torch
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def test_gather_pre_entities(self):
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model_name = self.small_models[0]
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
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sentence = "Hello there"
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tokens = tokenizer(
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sentence,
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return_attention_mask=False,
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return_tensors="pt",
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truncation=True,
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return_special_tokens_mask=True,
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return_offsets_mapping=True,
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)
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offset_mapping = tokens.pop("offset_mapping").cpu().numpy()[0]
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special_tokens_mask = tokens.pop("special_tokens_mask").cpu().numpy()[0]
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input_ids = tokens["input_ids"].numpy()[0]
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# First element in [CLS]
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scores = np.array([[1, 0, 0], [0.1, 0.3, 0.6], [0.8, 0.1, 0.1]])
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pre_entities = token_classifier.gather_pre_entities(
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sentence, input_ids, scores, offset_mapping, special_tokens_mask
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)
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self.assertEqual(
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nested_simplify(pre_entities),
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[
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{"word": "Hello", "scores": [0.1, 0.3, 0.6], "start": 0, "end": 5, "is_subword": False, "index": 1},
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{
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"word": "there",
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"scores": [0.8, 0.1, 0.1],
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"index": 2,
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"start": 6,
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"end": 11,
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"is_subword": False,
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},
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],
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)
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@require_tf
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def test_tf_only(self):
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model_name = "Narsil/small" # This model only has a TensorFlow version
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# We test that if we don't specificy framework='tf', it gets detected automatically
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token_classifier = pipeline(task="ner", model=model_name)
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self._test_pipeline(token_classifier)
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@require_tf
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def test_tf_defaults(self):
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="tf")
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self._test_pipeline(token_classifier)
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@require_tf
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def test_tf_small_ignore_subwords_available_for_fast_tokenizers(self):
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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token_classifier = pipeline(
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task="ner",
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model=model_name,
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tokenizer=tokenizer,
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framework="tf",
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aggregation_strategy=AggregationStrategy.FIRST,
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)
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self._test_pipeline(token_classifier)
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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token_classifier = pipeline(
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task="ner",
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model=model_name,
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tokenizer=tokenizer,
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framework="tf",
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aggregation_strategy=AggregationStrategy.SIMPLE,
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)
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self._test_pipeline(token_classifier)
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@require_torch
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def test_pt_ignore_subwords_slow_tokenizer_raises(self):
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model_name = self.small_models[0]
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
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with self.assertRaises(ValueError):
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pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.FIRST)
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with self.assertRaises(ValueError):
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pipeline(
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task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.AVERAGE
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)
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with self.assertRaises(ValueError):
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pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.MAX)
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@require_torch
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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)]])
|