06f1fd69a6
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507 lines
25 KiB
Python
507 lines
25 KiB
Python
# Copyright 2021 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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from transformers import LukeTokenizer
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from transformers.testing_utils import get_tests_dir, require_torch, slow
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/vocab.json")
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SAMPLE_MERGE_FILE = get_tests_dir("fixtures/merges.txt")
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SAMPLE_ENTITY_VOCAB = get_tests_dir("fixtures/test_entity_vocab.json")
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class LukeTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "studio-ousia/luke-base"
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tokenizer_class = LukeTokenizer
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from_pretrained_kwargs = {"cls_token": "<s>"}
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integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'Ġ', 'Ġ', 'ĠHello', 'ĊĊ', 'Ġ', 'Ċ', 'Ġ', 'Ġ', 'Ċ', 'ĠHello', 'Ċ', '<s>', 'Ċ', 'hi', '<s>', 'there', 'Ċ', 'The', 'Ġfollowing', 'Ġstring', 'Ġshould', 'Ġbe', 'Ġproperly', 'Ġencoded', ':', 'ĠHello', '.', 'Ċ', 'But', 'Ġ', 'ird', 'Ġand', 'Ġ', 'à¸', 'Ľ', 'à¸', 'µ', 'Ġ', 'Ġ', 'Ġ', 'ird', 'Ġ', 'Ġ', 'Ġ', 'à¸', 'Ķ', 'Ċ', 'Hey', 'Ġhow', 'Ġare', 'Ġyou', 'Ġdoing'] # fmt: skip
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integration_expected_token_ids = [713, 16, 10, 1296, 17841, 27969, 50118, 100, 21, 2421, 11, 8403, 151, 6, 8, 42, 16, 22461, 1140, 4, 50118, 48998, 37127, 20024, 2023, 44574, 49122, 4333, 36484, 7487, 3726, 48569, 50118, 30086, 1437, 20920, 50118, 30086, 1437, 1437, 20920, 50140, 1437, 50118, 1437, 1437, 50118, 20920, 50118, 0, 50118, 3592, 0, 8585, 50118, 133, 511, 6755, 197, 28, 5083, 45320, 35, 20920, 4, 50118, 1708, 1437, 8602, 8, 1437, 24107, 3726, 24107, 8906, 1437, 1437, 1437, 8602, 1437, 1437, 1437, 24107, 10674, 50118, 13368, 141, 32, 47, 608] # fmt: skip
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expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'Ġ', 'Ġ', 'ĠHello', 'ĊĊ', 'Ġ', 'Ċ', 'Ġ', 'Ġ', 'Ċ', 'ĠHello', 'Ċ', '<s>', 'Ċ', 'hi', '<s>', 'there', 'Ċ', 'The', 'Ġfollowing', 'Ġstring', 'Ġshould', 'Ġbe', 'Ġproperly', 'Ġencoded', ':', 'ĠHello', '.', 'Ċ', 'But', 'Ġ', 'ird', 'Ġand', 'Ġ', 'à¸', 'Ľ', 'à¸', 'µ', 'Ġ', 'Ġ', 'Ġ', 'ird', 'Ġ', 'Ġ', 'Ġ', 'à¸', 'Ķ', 'Ċ', 'Hey', 'Ġhow', 'Ġare', 'Ġyou', 'Ġdoing'] # fmt: skip
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integration_expected_decoded_text = "This is a test 😊\nI was born in 92000, and this is falsé.\n生活的真谛是\nHi Hello\nHi Hello\n\n \n \n Hello\n<s>\nhi<s>there\nThe following string should be properly encoded: Hello.\nBut ird and ปี ird ด\nHey how are you doing"
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@slow
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("studio-ousia/luke-large")
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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encoded_text_from_decode = tokenizer.encode(
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"sequence builders", add_special_tokens=True, add_prefix_space=False
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)
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encoded_pair_from_decode = tokenizer.encode(
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"sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False
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)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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self.assertEqual(encoded_sentence, encoded_text_from_decode)
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self.assertEqual(encoded_pair, encoded_pair_from_decode)
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def get_clean_sequence(self, tokenizer, max_length=20) -> tuple[str, list]:
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txt = "Beyonce lives in Los Angeles"
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ids = tokenizer.encode(txt, add_special_tokens=False)
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return txt, ids
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def test_padding_entity_inputs(self):
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tokenizer = self.get_tokenizer()
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sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
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span = (15, 34)
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pad_id = tokenizer.entity_vocab["[PAD]"]
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mask_id = tokenizer.entity_vocab["[MASK]"]
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encoding = tokenizer([sentence, sentence], entity_spans=[[span], [span, span]], padding=True)
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self.assertEqual(encoding["entity_ids"], [[mask_id, pad_id], [mask_id, mask_id]])
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# test with a sentence with no entity
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encoding = tokenizer([sentence, sentence], entity_spans=[[], [span, span]], padding=True)
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self.assertEqual(encoding["entity_ids"], [[pad_id, pad_id], [mask_id, mask_id]])
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@slow
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@require_torch
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class LukeTokenizerIntegrationTests(unittest.TestCase):
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tokenizer_class = LukeTokenizer
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from_pretrained_kwargs = {"cls_token": "<s>"}
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def setUp(self):
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super().setUp()
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def test_single_text_no_padding_or_truncation(self):
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tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", return_token_type_ids=True)
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sentence = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck."
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entities = ["Ana Ivanovic", "Thursday", "Dummy Entity"]
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spans = [(9, 21), (30, 38), (39, 42)]
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encoding = tokenizer(sentence, entities=entities, entity_spans=spans, return_token_type_ids=True)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
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"<s>Top seed Ana Ivanovic said on Thursday she could hardly believe her luck.</s>",
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][3:6], spaces_between_special_tokens=False), " Ana Ivanovic"
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][8:9], spaces_between_special_tokens=False), " Thursday"
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)
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self.assertEqual(tokenizer.decode(encoding["input_ids"][9:10], spaces_between_special_tokens=False), " she")
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self.assertEqual(
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encoding["entity_ids"],
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[
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tokenizer.entity_vocab["Ana Ivanovic"],
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tokenizer.entity_vocab["Thursday"],
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tokenizer.entity_vocab["[UNK]"],
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],
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)
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self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1])
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self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0])
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# fmt: off
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self.assertEqual(
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encoding["entity_position_ids"],
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[
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[3, 4, 5, -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],
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[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, -1, -1, -1, -1],
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[9, -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],
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]
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)
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# fmt: on
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def test_single_text_only_entity_spans_no_padding_or_truncation(self):
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tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", return_token_type_ids=True)
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sentence = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck."
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spans = [(9, 21), (30, 38), (39, 42)]
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encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
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"<s>Top seed Ana Ivanovic said on Thursday she could hardly believe her luck.</s>",
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][3:6], spaces_between_special_tokens=False), " Ana Ivanovic"
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][8:9], spaces_between_special_tokens=False), " Thursday"
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)
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self.assertEqual(tokenizer.decode(encoding["input_ids"][9:10], spaces_between_special_tokens=False), " she")
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mask_id = tokenizer.entity_vocab["[MASK]"]
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self.assertEqual(encoding["entity_ids"], [mask_id, mask_id, mask_id])
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self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1])
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self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0])
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# fmt: off
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self.assertEqual(
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encoding["entity_position_ids"],
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[
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[3, 4, 5, -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],
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[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, -1, -1, -1, -1, ],
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[9, -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, ]
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]
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)
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# fmt: on
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def test_single_text_padding_pytorch_tensors(self):
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tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", return_token_type_ids=True)
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sentence = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck."
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entities = ["Ana Ivanovic", "Thursday", "Dummy Entity"]
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spans = [(9, 21), (30, 38), (39, 42)]
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encoding = tokenizer(
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sentence,
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entities=entities,
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entity_spans=spans,
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return_token_type_ids=True,
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padding="max_length",
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max_length=30,
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max_entity_length=16,
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return_tensors="pt",
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)
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# test words
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self.assertEqual(encoding["input_ids"].shape, (1, 30))
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self.assertEqual(encoding["attention_mask"].shape, (1, 30))
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self.assertEqual(encoding["token_type_ids"].shape, (1, 30))
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# test entities
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self.assertEqual(encoding["entity_ids"].shape, (1, 16))
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self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
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self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
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self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length))
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def test_text_pair_no_padding_or_truncation(self):
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tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", return_token_type_ids=True)
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sentence = "Top seed Ana Ivanovic said on Thursday"
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sentence_pair = "She could hardly believe her luck."
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entities = ["Ana Ivanovic", "Thursday"]
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entities_pair = ["Dummy Entity"]
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spans = [(9, 21), (30, 38)]
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spans_pair = [(0, 3)]
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encoding = tokenizer(
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sentence,
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sentence_pair,
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entities=entities,
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entities_pair=entities_pair,
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entity_spans=spans,
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entity_spans_pair=spans_pair,
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return_token_type_ids=True,
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
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"<s>Top seed Ana Ivanovic said on Thursday</s></s>She could hardly believe her luck.</s>",
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][3:6], spaces_between_special_tokens=False), " Ana Ivanovic"
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][8:9], spaces_between_special_tokens=False), " Thursday"
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)
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self.assertEqual(tokenizer.decode(encoding["input_ids"][11:12], spaces_between_special_tokens=False), "She")
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self.assertEqual(
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encoding["entity_ids"],
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[
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tokenizer.entity_vocab["Ana Ivanovic"],
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tokenizer.entity_vocab["Thursday"],
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tokenizer.entity_vocab["[UNK]"],
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],
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)
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self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1])
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self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0])
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# fmt: off
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self.assertEqual(
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encoding["entity_position_ids"],
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[
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[3, 4, 5, -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],
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[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, -1, -1, -1, -1],
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[11, -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],
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]
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)
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# fmt: on
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def test_text_pair_only_entity_spans_no_padding_or_truncation(self):
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tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", return_token_type_ids=True)
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sentence = "Top seed Ana Ivanovic said on Thursday"
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sentence_pair = "She could hardly believe her luck."
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spans = [(9, 21), (30, 38)]
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spans_pair = [(0, 3)]
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encoding = tokenizer(
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sentence,
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sentence_pair,
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entity_spans=spans,
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entity_spans_pair=spans_pair,
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return_token_type_ids=True,
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
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"<s>Top seed Ana Ivanovic said on Thursday</s></s>She could hardly believe her luck.</s>",
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][3:6], spaces_between_special_tokens=False), " Ana Ivanovic"
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)
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self.assertEqual(
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tokenizer.decode(encoding["input_ids"][8:9], spaces_between_special_tokens=False), " Thursday"
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)
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self.assertEqual(tokenizer.decode(encoding["input_ids"][11:12], spaces_between_special_tokens=False), "She")
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mask_id = tokenizer.entity_vocab["[MASK]"]
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self.assertEqual(encoding["entity_ids"], [mask_id, mask_id, mask_id])
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self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1])
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self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0])
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# fmt: off
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self.assertEqual(
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encoding["entity_position_ids"],
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[
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[3, 4, 5, -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],
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[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, -1, -1, -1, -1],
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[11, -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],
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]
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)
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# fmt: on
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def test_text_pair_padding_pytorch_tensors(self):
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tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", return_token_type_ids=True)
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sentence = "Top seed Ana Ivanovic said on Thursday"
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||
sentence_pair = "She could hardly believe her luck."
|
||
entities = ["Ana Ivanovic", "Thursday"]
|
||
entities_pair = ["Dummy Entity"]
|
||
spans = [(9, 21), (30, 38)]
|
||
spans_pair = [(0, 3)]
|
||
|
||
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=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_entity_classification_no_padding_or_truncation(self):
|
||
tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", task="entity_classification")
|
||
sentence = (
|
||
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped"
|
||
" the new world number one avoid a humiliating second- round exit at Wimbledon ."
|
||
)
|
||
span = (39, 42)
|
||
|
||
encoding = tokenizer(sentence, entity_spans=[span], return_token_type_ids=True)
|
||
|
||
# test words
|
||
self.assertEqual(len(encoding["input_ids"]), 42)
|
||
self.assertEqual(len(encoding["attention_mask"]), 42)
|
||
self.assertEqual(len(encoding["token_type_ids"]), 42)
|
||
self.assertEqual(
|
||
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
|
||
"<s>Top seed Ana Ivanovic said on Thursday<ent> she<ent> could hardly believe her luck as a fortuitous"
|
||
" netcord helped the new world number one avoid a humiliating second- round exit at Wimbledon.</s>",
|
||
)
|
||
self.assertEqual(
|
||
tokenizer.decode(encoding["input_ids"][9:12], spaces_between_special_tokens=False), "<ent> she<ent>"
|
||
)
|
||
|
||
# test entities
|
||
self.assertEqual(encoding["entity_ids"], [2])
|
||
self.assertEqual(encoding["entity_attention_mask"], [1])
|
||
self.assertEqual(encoding["entity_token_type_ids"], [0])
|
||
# fmt: off
|
||
self.assertEqual(
|
||
encoding["entity_position_ids"],
|
||
[
|
||
[9, 10, 11, -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_classification_padding_pytorch_tensors(self):
|
||
tokenizer = LukeTokenizer.from_pretrained(
|
||
"studio-ousia/luke-base", task="entity_classification", return_token_type_ids=True
|
||
)
|
||
sentence = (
|
||
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped"
|
||
" the new world number one avoid a humiliating second- round exit at Wimbledon ."
|
||
)
|
||
# entity information
|
||
span = (39, 42)
|
||
|
||
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 = LukeTokenizer.from_pretrained(
|
||
"studio-ousia/luke-base", task="entity_pair_classification", return_token_type_ids=True
|
||
)
|
||
sentence = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck."
|
||
# head and tail information
|
||
spans = [(9, 21), (39, 42)]
|
||
|
||
encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True)
|
||
|
||
self.assertEqual(
|
||
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
|
||
"<s>Top seed<ent> Ana Ivanovic<ent> said on Thursday<ent2> she<ent2> could hardly believe her luck.</s>",
|
||
)
|
||
self.assertEqual(
|
||
tokenizer.decode(encoding["input_ids"][3:8], spaces_between_special_tokens=False),
|
||
"<ent> Ana Ivanovic<ent>",
|
||
)
|
||
self.assertEqual(
|
||
tokenizer.decode(encoding["input_ids"][11:14], spaces_between_special_tokens=False), "<ent2> she<ent2>"
|
||
)
|
||
|
||
self.assertEqual(encoding["entity_ids"], [2, 3])
|
||
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"],
|
||
[
|
||
[3, 4, 5, 6, 7, -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],
|
||
[11, 12, 13, -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 = LukeTokenizer.from_pretrained(
|
||
"studio-ousia/luke-base", task="entity_pair_classification", return_token_type_ids=True
|
||
)
|
||
sentence = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck."
|
||
# head and tail information
|
||
spans = [(9, 21), (39, 42)]
|
||
|
||
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 = LukeTokenizer.from_pretrained(
|
||
"studio-ousia/luke-base", task="entity_span_classification", return_token_type_ids=True
|
||
)
|
||
sentence = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck."
|
||
spans = [(0, 8), (9, 21), (39, 42)]
|
||
|
||
encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True)
|
||
|
||
self.assertEqual(
|
||
tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
|
||
"<s>Top seed Ana Ivanovic said on Thursday she could hardly believe her luck.</s>",
|
||
)
|
||
|
||
self.assertEqual(encoding["entity_ids"], [2, 2, 2])
|
||
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, 2, -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],
|
||
[3, 4, 5, -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],
|
||
[9, -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, 3, 9])
|
||
self.assertEqual(encoding["entity_end_positions"], [2, 5, 9])
|
||
|
||
def test_entity_span_classification_padding_pytorch_tensors(self):
|
||
tokenizer = LukeTokenizer.from_pretrained(
|
||
"studio-ousia/luke-base", task="entity_span_classification", return_token_type_ids=True
|
||
)
|
||
sentence = "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck."
|
||
spans = [(0, 8), (9, 21), (39, 42)]
|
||
|
||
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))
|