# Copyright 2021 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 from functools import lru_cache from transformers.models.mluke.tokenization_mluke import MLukeTokenizer from transformers.testing_utils import get_tests_dir, require_torch, slow from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") SAMPLE_ENTITY_VOCAB = get_tests_dir("fixtures/test_entity_vocab.json") class MLukeTokenizerTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "studio-ousia/mluke-base" tokenizer_class = MLukeTokenizer test_rust_tokenizer = False from_pretrained_kwargs = {"cls_token": ""} @classmethod def setUpClass(cls): super().setUpClass() cls.special_tokens_map = {"entity_token_1": "", "entity_token_2": ""} @classmethod @use_cache_if_possible @lru_cache(maxsize=64) def get_tokenizer(cls, task=None, **kwargs): kwargs.update(cls.special_tokens_map) kwargs.update({"task": task}) tokenizer = MLukeTokenizer(vocab_file=SAMPLE_VOCAB, entity_vocab_file=SAMPLE_ENTITY_VOCAB, **kwargs) return tokenizer def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.get_tokenizer() text = "lower newer" spm_tokens = ["▁l", "ow", "er", "▁new", "er"] tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, spm_tokens) input_tokens = tokens + [tokenizer.unk_token] input_spm_tokens = [149, 116, 40, 410, 40] + [3] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_spm_tokens) def mluke_dict_integration_testing(self): tokenizer = self.get_tokenizer() self.assertListEqual(tokenizer.encode("Hello world!", add_special_tokens=False), [35378, 8999, 38]) self.assertListEqual( tokenizer.encode("Hello world! cécé herlolip 418", add_special_tokens=False), [35378, 8999, 38, 33273, 11676, 604, 365, 21392, 201, 1819], ) def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("hf-internal-testing/tiny-random-mluke") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_text_from_decode = tokenizer.encode( "sequence builders", add_special_tokens=True, add_prefix_space=False ) encoded_pair_from_decode = tokenizer.encode( "sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False ) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) self.assertEqual(encoded_sentence, encoded_text_from_decode) self.assertEqual(encoded_pair, encoded_pair_from_decode) def get_clean_sequence(self, tokenizer, max_length=20) -> tuple[str, list]: txt = "Beyonce lives in Los Angeles" ids = tokenizer.encode(txt, add_special_tokens=False) return txt, ids @unittest.skip def test_pretokenized_inputs(self): pass def test_embeded_special_tokens(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs) tokenizer_p = self.get_tokenizer(pretrained_name, **kwargs) sentence = "A, AllenNLP sentence." tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) # token_type_ids should put 0 everywhere self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"])) # attention_mask should put 1 everywhere, so sum over length should be 1 self.assertEqual( sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]), sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]), ) tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]) # Rust correctly handles the space before the mask while python doesn't self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) self.assertSequenceEqual( tokens_p_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] ) def test_padding_entity_inputs(self): tokenizer = self.get_tokenizer() sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." span = (15, 34) pad_id = tokenizer.entity_vocab["[PAD]"] mask_id = tokenizer.entity_vocab["[MASK]"] encoding = tokenizer([sentence, sentence], entity_spans=[[span], [span, span]], padding=True) self.assertEqual(encoding["entity_ids"], [[mask_id, pad_id], [mask_id, mask_id]]) # test with a sentence with no entity encoding = tokenizer([sentence, sentence], entity_spans=[[], [span, span]], padding=True) self.assertEqual(encoding["entity_ids"], [[pad_id, pad_id], [mask_id, mask_id]]) def test_if_tokenize_single_text_raise_error_with_invalid_inputs(self): tokenizer = self.get_tokenizer() sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and Afghanistan." entities = ["DUMMY"] spans = [(0, 9)] with self.assertRaises(ValueError): tokenizer(sentence, entities=tuple(entities), entity_spans=spans) with self.assertRaises(TypeError): tokenizer(sentence, entities=entities, entity_spans=tuple(spans)) with self.assertRaises(ValueError): tokenizer(sentence, entities=[0], entity_spans=spans) with self.assertRaises(ValueError): tokenizer(sentence, entities=entities, entity_spans=[0]) with self.assertRaises(ValueError): tokenizer(sentence, entities=entities, entity_spans=spans + [(0, 9)]) def test_if_tokenize_entity_classification_raise_error_with_invalid_inputs(self): tokenizer = self.get_tokenizer(task="entity_classification") sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." span = (15, 34) with self.assertRaises(ValueError): tokenizer(sentence, entity_spans=[]) with self.assertRaises(ValueError): tokenizer(sentence, entity_spans=[span, span]) with self.assertRaises(ValueError): tokenizer(sentence, entity_spans=[0]) def test_if_tokenize_entity_pair_classification_raise_error_with_invalid_inputs(self): tokenizer = self.get_tokenizer(task="entity_pair_classification") sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." # head and tail information with self.assertRaises(ValueError): tokenizer(sentence, entity_spans=[]) with self.assertRaises(ValueError): tokenizer(sentence, entity_spans=[0, 0]) def test_if_tokenize_entity_span_classification_raise_error_with_invalid_inputs(self): tokenizer = self.get_tokenizer(task="entity_span_classification") sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." with self.assertRaises(ValueError): tokenizer(sentence, entity_spans=[]) with self.assertRaises(ValueError): tokenizer(sentence, entity_spans=[0, 0, 0]) @slow @require_torch class MLukeTokenizerIntegrationTests(unittest.TestCase): tokenizer_class = MLukeTokenizer from_pretrained_kwargs = {"cls_token": ""} @classmethod def setUpClass(cls): cls.tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base", return_token_type_ids=True) cls.entity_classification_tokenizer = MLukeTokenizer.from_pretrained( "studio-ousia/mluke-base", return_token_type_ids=True, task="entity_classification" ) cls.entity_pair_tokenizer = MLukeTokenizer.from_pretrained( "studio-ousia/mluke-base", return_token_type_ids=True, task="entity_pair_classification" ) cls.entity_span_tokenizer = MLukeTokenizer.from_pretrained( "studio-ousia/mluke-base", return_token_type_ids=True, task="entity_span_classification" ) def test_single_text_no_padding_or_truncation(self): tokenizer = self.tokenizer sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)." entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"] spans = [(0, 9), (59, 63), (68, 75), (77, 88)] encoding = tokenizer(sentence, entities=entities, entity_spans=spans, return_token_type_ids=True) self.assertEqual( tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False), " ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).", ) self.assertEqual( tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3" ) self.assertEqual(tokenizer.decode(encoding["input_ids"][17], spaces_between_special_tokens=False), "Iran") self.assertEqual( tokenizer.decode(encoding["input_ids"][19:25], spaces_between_special_tokens=False), "アフガニスタン" ) self.assertEqual( tokenizer.decode(encoding["input_ids"][26], spaces_between_special_tokens=False), "Afghanistan" ) self.assertEqual( encoding["entity_ids"], [ tokenizer.entity_vocab["en:ISO 639-3"], tokenizer.entity_vocab["[UNK]"], tokenizer.entity_vocab["ja:アフガニスタン"], tokenizer.entity_vocab["en:Afghanistan"], ], ) self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1]) self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0]) # fmt: off self.assertEqual( encoding["entity_position_ids"], [ [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [17, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [19, 20, 21, 22, 23, 24, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] ] ) # fmt: on def test_single_text_only_entity_spans_no_padding_or_truncation(self): tokenizer = self.tokenizer sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)." entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"] spans = [(0, 9), (59, 63), (68, 75), (77, 88)] encoding = tokenizer(sentence, entities=entities, entity_spans=spans, return_token_type_ids=True) self.assertEqual( tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False), " ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).", ) self.assertEqual( tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3" ) self.assertEqual(tokenizer.decode(encoding["input_ids"][17], spaces_between_special_tokens=False), "Iran") self.assertEqual( tokenizer.decode(encoding["input_ids"][20:25], spaces_between_special_tokens=False), "アフガニスタン" ) self.assertEqual( tokenizer.decode(encoding["input_ids"][26], spaces_between_special_tokens=False), "Afghanistan" ) self.assertEqual( encoding["entity_ids"], [ tokenizer.entity_vocab["en:ISO 639-3"], tokenizer.entity_vocab["[UNK]"], tokenizer.entity_vocab["ja:アフガニスタン"], tokenizer.entity_vocab["en:Afghanistan"], ], ) self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1]) self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0]) # fmt: off self.assertEqual( encoding["entity_position_ids"], [ [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [17, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [19, 20, 21, 22, 23, 24, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] ] ) # fmt: on def test_single_text_padding_pytorch_tensors(self): tokenizer = self.tokenizer sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)." entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"] spans = [(0, 9), (59, 63), (68, 75), (77, 88)] encoding = tokenizer( sentence, entities=entities, entity_spans=spans, return_token_type_ids=True, padding="max_length", max_length=30, max_entity_length=16, return_tensors="pt", ) # test words self.assertEqual(encoding["input_ids"].shape, (1, 30)) self.assertEqual(encoding["attention_mask"].shape, (1, 30)) self.assertEqual(encoding["token_type_ids"].shape, (1, 30)) # test entities self.assertEqual(encoding["entity_ids"].shape, (1, 16)) self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16)) self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16)) self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length)) def test_text_pair_no_padding_or_truncation(self): tokenizer = self.tokenizer sentence = "ISO 639-3 uses the code fas" sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)." entities = ["en:ISO 639-3"] entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"] spans = [(0, 9)] spans_pair = [(31, 35), (40, 47), (49, 60)] encoding = tokenizer( sentence, sentence_pair, entities=entities, entities_pair=entities_pair, entity_spans=spans, entity_spans_pair=spans_pair, return_token_type_ids=True, ) self.assertEqual( tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False), " ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan" " ).", ) self.assertEqual( tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3" ) self.assertEqual(tokenizer.decode(encoding["input_ids"][19], spaces_between_special_tokens=False), "Iran") self.assertEqual( tokenizer.decode(encoding["input_ids"][21:27], spaces_between_special_tokens=False), "アフガニスタン" ) self.assertEqual( tokenizer.decode(encoding["input_ids"][28], spaces_between_special_tokens=False), "Afghanistan" ) self.assertEqual( encoding["entity_ids"], [ tokenizer.entity_vocab["en:ISO 639-3"], tokenizer.entity_vocab["[UNK]"], tokenizer.entity_vocab["ja:アフガニスタン"], tokenizer.entity_vocab["en:Afghanistan"], ], ) self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1]) self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0]) # fmt: off self.assertEqual( encoding["entity_position_ids"], [ [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [19, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [21, 22, 23, 24, 25, 26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [28, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] ] ) # fmt: on def test_text_pair_only_entity_spans_no_padding_or_truncation(self): tokenizer = self.tokenizer sentence = "ISO 639-3 uses the code fas" sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)." entities = ["en:ISO 639-3"] entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"] spans = [(0, 9)] spans_pair = [(31, 35), (40, 47), (49, 60)] encoding = tokenizer( sentence, sentence_pair, entities=entities, entities_pair=entities_pair, entity_spans=spans, entity_spans_pair=spans_pair, return_token_type_ids=True, ) self.assertEqual( tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False), " ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan" " ).", ) self.assertEqual( tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3" ) self.assertEqual(tokenizer.decode(encoding["input_ids"][19], spaces_between_special_tokens=False), "Iran") self.assertEqual( tokenizer.decode(encoding["input_ids"][21:27], spaces_between_special_tokens=False), "アフガニスタン" ) self.assertEqual( tokenizer.decode(encoding["input_ids"][28], spaces_between_special_tokens=False), "Afghanistan" ) self.assertEqual( encoding["entity_ids"], [ tokenizer.entity_vocab["en:ISO 639-3"], tokenizer.entity_vocab["[UNK]"], tokenizer.entity_vocab["ja:アフガニスタン"], tokenizer.entity_vocab["en:Afghanistan"], ], ) # fmt: off self.assertEqual( encoding["entity_position_ids"], [ [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [19, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [21, 22, 23, 24, 25, 26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [28, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] ] ) # fmt: on def test_text_pair_padding_pytorch_tensors(self): tokenizer = self.tokenizer sentence = "ISO 639-3 uses the code fas" sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)." entities = ["en:ISO 639-3"] entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"] spans = [(0, 9)] spans_pair = [(31, 35), (40, 47), (49, 60)] encoding = tokenizer( sentence, sentence_pair, entities=entities, entities_pair=entities_pair, entity_spans=spans, entity_spans_pair=spans_pair, return_token_type_ids=True, padding="max_length", max_length=40, max_entity_length=16, return_tensors="pt", ) # test words self.assertEqual(encoding["input_ids"].shape, (1, 40)) self.assertEqual(encoding["attention_mask"].shape, (1, 40)) self.assertEqual(encoding["token_type_ids"].shape, (1, 40)) # test entities self.assertEqual(encoding["entity_ids"].shape, (1, 16)) self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16)) self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16)) self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length)) def test_entity_classification_no_padding_or_truncation(self): tokenizer = self.entity_classification_tokenizer sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." span = (15, 34) encoding = tokenizer(sentence, entity_spans=[span], return_token_type_ids=True) # test words self.assertEqual(len(encoding["input_ids"]), 23) self.assertEqual(len(encoding["attention_mask"]), 23) self.assertEqual(len(encoding["token_type_ids"]), 23) self.assertEqual( tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False), " Japanese is anEast Asian languagespoken by about 128 million people, primarily in" " Japan.", ) self.assertEqual( tokenizer.decode(encoding["input_ids"][4:9], spaces_between_special_tokens=False), "East Asian language", ) # test entities mask_id = tokenizer.entity_vocab["[MASK]"] self.assertEqual(encoding["entity_ids"], [mask_id]) self.assertEqual(encoding["entity_attention_mask"], [1]) self.assertEqual(encoding["entity_token_type_ids"], [0]) # fmt: off self.assertEqual( encoding["entity_position_ids"], [[4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]] ) # fmt: on def test_entity_classification_padding_pytorch_tensors(self): tokenizer = self.entity_classification_tokenizer sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." span = (15, 34) encoding = tokenizer( sentence, entity_spans=[span], return_token_type_ids=True, padding="max_length", return_tensors="pt" ) # test words self.assertEqual(encoding["input_ids"].shape, (1, 512)) self.assertEqual(encoding["attention_mask"].shape, (1, 512)) self.assertEqual(encoding["token_type_ids"].shape, (1, 512)) # test entities self.assertEqual(encoding["entity_ids"].shape, (1, 1)) self.assertEqual(encoding["entity_attention_mask"].shape, (1, 1)) self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 1)) self.assertEqual( encoding["entity_position_ids"].shape, (1, tokenizer.max_entity_length, tokenizer.max_mention_length) ) def test_entity_pair_classification_no_padding_or_truncation(self): tokenizer = self.entity_pair_tokenizer sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." # head and tail information spans = [(0, 8), (84, 89)] encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True) self.assertEqual( tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False), "Japaneseis an East Asian language spoken by about 128 million people, primarily" " inJapan.", ) self.assertEqual( tokenizer.decode(encoding["input_ids"][1:4], spaces_between_special_tokens=False), "Japanese", ) self.assertEqual( tokenizer.decode(encoding["input_ids"][20:23], spaces_between_special_tokens=False), "Japan" ) mask_id = tokenizer.entity_vocab["[MASK]"] mask2_id = tokenizer.entity_vocab["[MASK2]"] self.assertEqual(encoding["entity_ids"], [mask_id, mask2_id]) self.assertEqual(encoding["entity_attention_mask"], [1, 1]) self.assertEqual(encoding["entity_token_type_ids"], [0, 0]) # fmt: off self.assertEqual( encoding["entity_position_ids"], [ [1, 2, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [20, 21, 22, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] ] ) # fmt: on def test_entity_pair_classification_padding_pytorch_tensors(self): tokenizer = self.entity_pair_tokenizer sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." # head and tail information spans = [(0, 8), (84, 89)] encoding = tokenizer( sentence, entity_spans=spans, return_token_type_ids=True, padding="max_length", max_length=30, return_tensors="pt", ) # test words self.assertEqual(encoding["input_ids"].shape, (1, 30)) self.assertEqual(encoding["attention_mask"].shape, (1, 30)) self.assertEqual(encoding["token_type_ids"].shape, (1, 30)) # test entities self.assertEqual(encoding["entity_ids"].shape, (1, 2)) self.assertEqual(encoding["entity_attention_mask"].shape, (1, 2)) self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 2)) self.assertEqual( encoding["entity_position_ids"].shape, (1, tokenizer.max_entity_length, tokenizer.max_mention_length) ) def test_entity_span_classification_no_padding_or_truncation(self): tokenizer = self.entity_span_tokenizer sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." spans = [(0, 8), (15, 34), (84, 89)] encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True) self.assertEqual( tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False), " Japanese is an East Asian language spoken by about 128 million people, primarily in Japan.", ) mask_id = tokenizer.entity_vocab["[MASK]"] self.assertEqual(encoding["entity_ids"], [mask_id, mask_id, mask_id]) self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1]) self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0]) # fmt: off self.assertEqual( encoding["entity_position_ids"], [ [1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [4, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [18, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]] ) # fmt: on self.assertEqual(encoding["entity_start_positions"], [1, 4, 18]) self.assertEqual(encoding["entity_end_positions"], [1, 6, 18]) def test_entity_span_classification_padding_pytorch_tensors(self): tokenizer = self.entity_span_tokenizer sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan." spans = [(0, 8), (15, 34), (84, 89)] encoding = tokenizer( sentence, entity_spans=spans, return_token_type_ids=True, padding="max_length", max_length=30, max_entity_length=16, return_tensors="pt", ) # test words self.assertEqual(encoding["input_ids"].shape, (1, 30)) self.assertEqual(encoding["attention_mask"].shape, (1, 30)) self.assertEqual(encoding["token_type_ids"].shape, (1, 30)) # test entities self.assertEqual(encoding["entity_ids"].shape, (1, 16)) self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16)) self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16)) self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length)) self.assertEqual(encoding["entity_start_positions"].shape, (1, 16)) self.assertEqual(encoding["entity_end_positions"].shape, (1, 16))