mirror of
https://github.com/huggingface/transformers.git
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042f420364
* Update pipeline word heuristic to work with whitespace in token offsets This change checks for whitespace in the input string at either the character preceding the token or in the first character of the token. This works with tokenizers that return offsets excluding whitespace between words or with offsets including whitespace. fixes #18111 starting * Use smaller model, ensure expected tokenization * Re-run CI (please squash)
825 lines
34 KiB
Python
825 lines
34 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 (
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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AutoModelForTokenClassification,
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AutoTokenizer,
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TokenClassificationPipeline,
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pipeline,
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)
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from transformers.pipelines import AggregationStrategy, TokenClassificationArgumentHandler
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from transformers.testing_utils import (
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is_pipeline_test,
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nested_simplify,
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require_tf,
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require_torch,
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require_torch_gpu,
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slow,
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)
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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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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@is_pipeline_test
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class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
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tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
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return token_classifier, ["A simple string", "A simple string that is quite a bit longer"]
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def run_pipeline_test(self, token_classifier, _):
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model = token_classifier.model
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tokenizer = token_classifier.tokenizer
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outputs = token_classifier("A simple string")
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self.assertIsInstance(outputs, list)
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n = len(outputs)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{
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"entity": ANY(str),
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"score": ANY(float),
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"start": ANY(int),
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"end": ANY(int),
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"index": ANY(int),
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"word": ANY(str),
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}
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for i in range(n)
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],
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)
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outputs = token_classifier(["list of strings", "A simple string that is quite a bit longer"])
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self.assertIsInstance(outputs, list)
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self.assertEqual(len(outputs), 2)
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n = len(outputs[0])
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m = len(outputs[1])
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self.assertEqual(
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nested_simplify(outputs),
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[
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[
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{
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"entity": ANY(str),
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"score": ANY(float),
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"start": ANY(int),
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"end": ANY(int),
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"index": ANY(int),
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"word": ANY(str),
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}
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for i in range(n)
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],
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[
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{
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"entity": ANY(str),
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"score": ANY(float),
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"start": ANY(int),
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"end": ANY(int),
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"index": ANY(int),
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"word": ANY(str),
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}
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for i in range(m)
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],
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],
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)
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self.run_aggregation_strategy(model, tokenizer)
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def run_aggregation_strategy(self, model, tokenizer):
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token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="simple")
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self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
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outputs = token_classifier("A simple string")
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self.assertIsInstance(outputs, list)
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n = len(outputs)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{
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"entity_group": ANY(str),
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"score": ANY(float),
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"start": ANY(int),
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"end": ANY(int),
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"word": ANY(str),
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}
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for i in range(n)
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],
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)
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token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="first")
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self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
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outputs = token_classifier("A simple string")
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self.assertIsInstance(outputs, list)
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n = len(outputs)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{
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"entity_group": ANY(str),
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"score": ANY(float),
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"start": ANY(int),
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"end": ANY(int),
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"word": ANY(str),
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}
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for i in range(n)
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],
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)
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token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max")
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self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.MAX)
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outputs = token_classifier("A simple string")
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self.assertIsInstance(outputs, list)
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n = len(outputs)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{
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"entity_group": ANY(str),
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"score": ANY(float),
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"start": ANY(int),
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"end": ANY(int),
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"word": ANY(str),
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}
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for i in range(n)
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],
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)
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token_classifier = TokenClassificationPipeline(
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model=model, tokenizer=tokenizer, aggregation_strategy="average"
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)
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self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.AVERAGE)
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outputs = token_classifier("A simple string")
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self.assertIsInstance(outputs, list)
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n = len(outputs)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{
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"entity_group": ANY(str),
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"score": ANY(float),
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"start": ANY(int),
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"end": ANY(int),
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"word": ANY(str),
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}
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for i in range(n)
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],
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)
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with self.assertWarns(UserWarning):
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token_classifier = pipeline(task="ner", model=model, tokenizer=tokenizer, grouped_entities=True)
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self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
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with self.assertWarns(UserWarning):
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token_classifier = pipeline(
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task="ner", model=model, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
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)
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self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
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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_gpu
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@slow
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def test_gpu(self):
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sentence = "This is dummy sentence"
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ner = pipeline(
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"token-classification",
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device=0,
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aggregation_strategy=AggregationStrategy.SIMPLE,
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)
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output = ner(sentence)
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self.assertEqual(nested_simplify(output), [])
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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 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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@slow
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def test_aggregation_strategy_byte_level_tokenizer(self):
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sentence = "Groenlinks praat over Schiphol."
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ner = pipeline("ner", model="xlm-roberta-large-finetuned-conll02-dutch", aggregation_strategy="max")
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self.assertEqual(
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nested_simplify(ner(sentence)),
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[
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{"end": 10, "entity_group": "ORG", "score": 0.994, "start": 0, "word": "Groenlinks"},
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{"entity_group": "LOC", "score": 1.0, "word": "Schiphol.", "start": 22, "end": 31},
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],
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)
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@require_torch
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def test_aggregation_strategy_no_b_i_prefix(self):
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model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
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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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token_classifier.model.config.id2label = {0: "O", 1: "MISC", 2: "PER", 3: "ORG", 4: "LOC"}
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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]),
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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]),
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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, 0.9986497163772583, 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": "LOC", "score": 0.997, "start": 0, "word": "En", "index": 1},
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{"end": 4, "entity": "LOC", "score": 0.996, "start": 2, "word": "##zo", "index": 2},
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{"end": 13, "entity": "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": "LOC", "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(self):
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model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
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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},
|
|
{"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 = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
|
|
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
|
|
@slow
|
|
def test_aggregation_strategy_offsets_with_leading_space(self):
|
|
sentence = "We're from New York"
|
|
model_name = "brandon25/deberta-base-finetuned-ner"
|
|
ner = pipeline("ner", model=model_name, ignore_labels=[], aggregation_strategy="max")
|
|
self.assertEqual(
|
|
nested_simplify(ner(sentence)),
|
|
[
|
|
{"entity_group": "O", "score": 1.0, "word": " We're from", "start": 0, "end": 10},
|
|
{"entity_group": "LOC", "score": 1.0, "word": " New York", "start": 10, "end": 19},
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
def test_gather_pre_entities(self):
|
|
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
|
|
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,
|
|
aggregation_strategy=AggregationStrategy.NONE,
|
|
)
|
|
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_torch
|
|
def test_word_heuristic_leading_space(self):
|
|
model_name = "hf-internal-testing/tiny-random-deberta-v2"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
|
|
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
|
|
|
|
sentence = "I play the theremin"
|
|
|
|
tokens = tokenizer(
|
|
sentence,
|
|
return_attention_mask=False,
|
|
return_tensors="pt",
|
|
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]
|
|
scores = np.array([[1, 0] for _ in input_ids]) # values irrelevant for heuristic
|
|
|
|
pre_entities = token_classifier.gather_pre_entities(
|
|
sentence,
|
|
input_ids,
|
|
scores,
|
|
offset_mapping,
|
|
special_tokens_mask,
|
|
aggregation_strategy=AggregationStrategy.FIRST,
|
|
)
|
|
|
|
# ensure expected tokenization and correct is_subword values
|
|
self.assertEqual(
|
|
[(entity["word"], entity["is_subword"]) for entity in pre_entities],
|
|
[("▁I", False), ("▁play", False), ("▁the", False), ("▁there", False), ("min", True)],
|
|
)
|
|
|
|
@require_tf
|
|
def test_tf_only(self):
|
|
model_name = "hf-internal-testing/tiny-random-bert-tf-only" # 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.assertEqual(token_classifier.framework, "tf")
|
|
|
|
@require_tf
|
|
def test_small_model_tf(self):
|
|
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
|
|
token_classifier = pipeline(task="token-classification", model=model_name, framework="tf")
|
|
outputs = token_classifier("This is a test !")
|
|
self.assertEqual(
|
|
nested_simplify(outputs),
|
|
[
|
|
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
|
|
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
def test_no_offset_tokenizer(self):
|
|
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
|
|
token_classifier = pipeline(task="token-classification", model=model_name, tokenizer=tokenizer, framework="pt")
|
|
outputs = token_classifier("This is a test !")
|
|
self.assertEqual(
|
|
nested_simplify(outputs),
|
|
[
|
|
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": None, "end": None},
|
|
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": None, "end": None},
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
def test_small_model_pt(self):
|
|
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
|
|
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt")
|
|
outputs = token_classifier("This is a test !")
|
|
self.assertEqual(
|
|
nested_simplify(outputs),
|
|
[
|
|
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
|
|
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
|
|
],
|
|
)
|
|
|
|
token_classifier = pipeline(
|
|
task="token-classification", model=model_name, framework="pt", ignore_labels=["O", "I-MISC"]
|
|
)
|
|
outputs = token_classifier("This is a test !")
|
|
self.assertEqual(
|
|
nested_simplify(outputs),
|
|
[],
|
|
)
|
|
|
|
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt")
|
|
# Overload offset_mapping
|
|
outputs = token_classifier(
|
|
"This is a test !", offset_mapping=[(0, 0), (0, 1), (0, 2), (0, 0), (0, 0), (0, 0), (0, 0)]
|
|
)
|
|
self.assertEqual(
|
|
nested_simplify(outputs),
|
|
[
|
|
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 1},
|
|
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 0, "end": 2},
|
|
],
|
|
)
|
|
|
|
# Batch size does not affect outputs (attention_mask are required)
|
|
sentences = ["This is a test !", "Another test this is with longer sentence"]
|
|
outputs = token_classifier(sentences)
|
|
outputs_batched = token_classifier(sentences, batch_size=2)
|
|
# Batching does not make a difference in predictions
|
|
self.assertEqual(nested_simplify(outputs_batched), nested_simplify(outputs))
|
|
self.assertEqual(
|
|
nested_simplify(outputs_batched),
|
|
[
|
|
[
|
|
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
|
|
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
|
|
],
|
|
[],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
def test_pt_ignore_subwords_slow_tokenizer_raises(self):
|
|
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
|
|
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)
|
|
|
|
@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},
|
|
],
|
|
[],
|
|
],
|
|
)
|
|
|
|
|
|
@is_pipeline_test
|
|
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)]])
|