# 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 ( MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline, pipeline, ) from transformers.pipelines import AggregationStrategy, TokenClassificationArgumentHandler from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_tf, require_torch, require_torch_gpu, slow, ) from .test_pipelines_common import ANY VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]] # These 2 model types require different inputs than those of the usual text models. _TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"} @is_pipeline_test class TokenClassificationPipelineTests(unittest.TestCase): model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING if model_mapping is not None: model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP} if tf_model_mapping is not None: tf_model_mapping = { config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP } def get_test_pipeline(self, model, tokenizer, processor): token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer) return token_classifier, ["A simple string", "A simple string that is quite a bit longer"] def run_pipeline_test(self, token_classifier, _): model = token_classifier.model tokenizer = token_classifier.tokenizer if not tokenizer.is_fast: return # Slow tokenizers do not return offsets mappings, so this test will fail outputs = token_classifier("A simple string") self.assertIsInstance(outputs, list) n = len(outputs) self.assertEqual( nested_simplify(outputs), [ { "entity": ANY(str), "score": ANY(float), "start": ANY(int), "end": ANY(int), "index": ANY(int), "word": ANY(str), } for i in range(n) ], ) outputs = token_classifier(["list of strings", "A simple string that is quite a bit longer"]) self.assertIsInstance(outputs, list) self.assertEqual(len(outputs), 2) n = len(outputs[0]) m = len(outputs[1]) self.assertEqual( nested_simplify(outputs), [ [ { "entity": ANY(str), "score": ANY(float), "start": ANY(int), "end": ANY(int), "index": ANY(int), "word": ANY(str), } for i in range(n) ], [ { "entity": ANY(str), "score": ANY(float), "start": ANY(int), "end": ANY(int), "index": ANY(int), "word": ANY(str), } for i in range(m) ], ], ) self.run_aggregation_strategy(model, tokenizer) def run_aggregation_strategy(self, model, tokenizer): token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="simple") self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE) outputs = token_classifier("A simple string") self.assertIsInstance(outputs, list) n = len(outputs) self.assertEqual( nested_simplify(outputs), [ { "entity_group": ANY(str), "score": ANY(float), "start": ANY(int), "end": ANY(int), "word": ANY(str), } for i in range(n) ], ) token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="first") self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST) outputs = token_classifier("A simple string") self.assertIsInstance(outputs, list) n = len(outputs) self.assertEqual( nested_simplify(outputs), [ { "entity_group": ANY(str), "score": ANY(float), "start": ANY(int), "end": ANY(int), "word": ANY(str), } for i in range(n) ], ) token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max") self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.MAX) outputs = token_classifier("A simple string") self.assertIsInstance(outputs, list) n = len(outputs) self.assertEqual( nested_simplify(outputs), [ { "entity_group": ANY(str), "score": ANY(float), "start": ANY(int), "end": ANY(int), "word": ANY(str), } for i in range(n) ], ) token_classifier = TokenClassificationPipeline( model=model, tokenizer=tokenizer, aggregation_strategy="average" ) self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.AVERAGE) outputs = token_classifier("A simple string") self.assertIsInstance(outputs, list) n = len(outputs) self.assertEqual( nested_simplify(outputs), [ { "entity_group": ANY(str), "score": ANY(float), "start": ANY(int), "end": ANY(int), "word": ANY(str), } for i in range(n) ], ) with self.assertWarns(UserWarning): token_classifier = pipeline(task="ner", model=model, tokenizer=tokenizer, grouped_entities=True) self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE) with self.assertWarns(UserWarning): token_classifier = pipeline( task="ner", model=model, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True ) self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST) @slow @require_torch def test_chunking(self): NER_MODEL = "elastic/distilbert-base-uncased-finetuned-conll03-english" model = AutoModelForTokenClassification.from_pretrained(NER_MODEL) tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True) tokenizer.model_max_length = 10 stride = 5 sentence = ( "Hugging Face, Inc. is a French company that develops tools for building applications using machine learning. " "The company, based in New York City was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf." ) token_classifier = TokenClassificationPipeline( model=model, tokenizer=tokenizer, aggregation_strategy="simple", stride=stride ) output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30}, {"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174}, {"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205}, {"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222}, {"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239}, ], ) token_classifier = TokenClassificationPipeline( model=model, tokenizer=tokenizer, aggregation_strategy="first", stride=stride ) output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30}, {"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174}, {"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205}, {"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222}, {"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239}, ], ) token_classifier = TokenClassificationPipeline( model=model, tokenizer=tokenizer, aggregation_strategy="max", stride=stride ) output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30}, {"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174}, {"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205}, {"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222}, {"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239}, ], ) token_classifier = TokenClassificationPipeline( model=model, tokenizer=tokenizer, aggregation_strategy="average", stride=stride ) output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30}, {"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144}, {"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174}, {"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205}, {"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222}, {"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239}, ], ) @require_torch def test_chunking_fast(self): # Note: We cannot run the test on "conflicts" on the chunking. # The problem is that the model is random, and thus the results do heavily # depend on the chunking, so we cannot expect "abcd" and "bcd" to find # the same entities. We defer to slow tests for this. pipe = pipeline(model="hf-internal-testing/tiny-bert-for-token-classification") sentence = "The company, based in New York City was founded in 2016 by French entrepreneurs" results = pipe(sentence, aggregation_strategy="first") # This is what this random model gives on the full sentence self.assertEqual( nested_simplify(results), [ # This is 2 actual tokens {"end": 39, "entity_group": "MISC", "score": 0.115, "start": 31, "word": "city was"}, {"end": 79, "entity_group": "MISC", "score": 0.115, "start": 66, "word": "entrepreneurs"}, ], ) # This will force the tokenizer to split after "city was". pipe.tokenizer.model_max_length = 12 self.assertEqual( pipe.tokenizer.decode(pipe.tokenizer.encode(sentence, truncation=True)), "[CLS] the company, based in new york city was [SEP]", ) stride = 4 results = pipe(sentence, aggregation_strategy="first", stride=stride) self.assertEqual( nested_simplify(results), [ {"end": 39, "entity_group": "MISC", "score": 0.115, "start": 31, "word": "city was"}, # This is an extra entity found by this random model, but at least both original # entities are there {"end": 58, "entity_group": "MISC", "score": 0.115, "start": 56, "word": "by"}, {"end": 79, "entity_group": "MISC", "score": 0.115, "start": 66, "word": "entrepreneurs"}, ], ) @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_gpu @slow def test_gpu(self): sentence = "This is dummy sentence" ner = pipeline( "token-classification", device=0, aggregation_strategy=AggregationStrategy.SIMPLE, ) output = ner(sentence) self.assertEqual(nested_simplify(output), []) @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 UN""" token_classifier = pipeline("ner", model=model, tokenizer=tokenizer) output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity": "I-PER", "score": 0.998, "word": "En", "start": 0, "end": 2, "index": 1}, {"entity": "I-PER", "score": 0.997, "word": "##zo", "start": 2, "end": 4, "index": 2}, {"entity": "I-ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20, "index": 6}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20}, ], ) 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": 0.998, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20}, ], ) 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": 0.998, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20}, ], ) token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average") output = token_classifier(sentence) self.assertEqual( nested_simplify(output), [ {"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4}, {"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20}, ], ) @require_torch @slow def test_aggregation_strategy_byte_level_tokenizer(self): sentence = "Groenlinks praat over Schiphol." ner = pipeline("ner", model="xlm-roberta-large-finetuned-conll02-dutch", aggregation_strategy="max") self.assertEqual( nested_simplify(ner(sentence)), [ {"end": 10, "entity_group": "ORG", "score": 0.994, "start": 0, "word": "Groenlinks"}, {"entity_group": "LOC", "score": 1.0, "word": "Schiphol.", "start": 22, "end": 31}, ], ) @require_torch def test_aggregation_strategy_no_b_i_prefix(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 token_classifier.model.config.id2label = {0: "O", 1: "MISC", 2: "PER", 3: "ORG", 4: "LOC"} example = [ { # fmt : off "scores": np.array([0, 0, 0, 0, 0.9968166351318359]), "index": 1, "is_subword": False, "word": "En", "start": 0, "end": 2, }, { # fmt : off "scores": np.array([0, 0, 0, 0, 0.9957635998725891]), "index": 2, "is_subword": True, "word": "##zo", "start": 2, "end": 4, }, { # fmt: off "scores": np.array([0, 0, 0, 0.9986497163772583, 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": "LOC", "score": 0.997, "start": 0, "word": "En", "index": 1}, {"end": 4, "entity": "LOC", "score": 0.996, "start": 2, "word": "##zo", "index": 2}, {"end": 13, "entity": "ORG", "score": 0.999, "start": 11, "word": "UN", "index": 7}, ], ) self.assertEqual( nested_simplify(token_classifier.aggregate(example, AggregationStrategy.SIMPLE)), [ {"entity_group": "LOC", "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(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 = [ { # 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 = "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}, ], [], ], ) 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)]])