669 lines
30 KiB
Python
669 lines
30 KiB
Python
# coding=utf-8
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# 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 typing import Tuple
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from transformers import AddedToken, 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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test_rust_tokenizer = False
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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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self.special_tokens_map = {"entity_token_1": "<ent>", "entity_token_2": "<ent2>"}
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def get_tokenizer(self, task=None, **kwargs):
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kwargs.update(self.special_tokens_map)
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tokenizer = LukeTokenizer(
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vocab_file=SAMPLE_VOCAB,
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merges_file=SAMPLE_MERGE_FILE,
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entity_vocab_file=SAMPLE_ENTITY_VOCAB,
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task=task,
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**kwargs,
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)
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return tokenizer
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def get_input_output_texts(self, tokenizer):
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input_text = "lower newer"
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output_text = "lower newer"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = self.get_tokenizer()
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text = "lower newer"
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bpe_tokens = ["l", "o", "w", "er", "Ġ", "n", "e", "w", "er"]
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tokens = tokenizer.tokenize(text) # , add_prefix_space=True)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + [tokenizer.unk_token]
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input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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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_space_encoding(self):
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tokenizer = self.get_tokenizer()
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sequence = "Encode this sequence."
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space_encoding = tokenizer.byte_encoder[" ".encode("utf-8")[0]]
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# Testing encoder arguments
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encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=False)
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first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0]
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self.assertNotEqual(first_char, space_encoding)
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encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
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first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0]
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self.assertEqual(first_char, space_encoding)
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tokenizer.add_special_tokens({"bos_token": "<s>"})
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encoded = tokenizer.encode(sequence, add_special_tokens=True)
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first_char = tokenizer.convert_ids_to_tokens(encoded[1])[0]
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self.assertNotEqual(first_char, space_encoding)
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# Testing spaces after special tokens
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mask = "<mask>"
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tokenizer.add_special_tokens(
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{"mask_token": AddedToken(mask, lstrip=True, rstrip=False)}
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) # mask token has a left space
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mask_ind = tokenizer.convert_tokens_to_ids(mask)
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sequence = "Encode <mask> sequence"
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sequence_nospace = "Encode <mask>sequence"
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encoded = tokenizer.encode(sequence)
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mask_loc = encoded.index(mask_ind)
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first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
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self.assertEqual(first_char, space_encoding)
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encoded = tokenizer.encode(sequence_nospace)
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mask_loc = encoded.index(mask_ind)
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first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
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self.assertNotEqual(first_char, space_encoding)
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def test_pretokenized_inputs(self):
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pass
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def test_embeded_special_tokens(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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sentence = "A, <mask> AllenNLP sentence."
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tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
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tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
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# token_type_ids should put 0 everywhere
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self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
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# token_type_ids should put 0 everywhere
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self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
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# attention_mask should put 1 everywhere, so sum over length should be 1
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self.assertEqual(
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sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
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)
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tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
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# Rust correctly handles the space before the mask while python doesnt
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self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
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self.assertSequenceEqual(
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tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
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)
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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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def test_if_tokenize_single_text_raise_error_with_invalid_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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spans = [(15, 34)]
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entities = ["East Asian language"]
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with self.assertRaises(ValueError):
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tokenizer(sentence, entities=tuple(entities), entity_spans=spans)
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with self.assertRaises(ValueError):
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tokenizer(sentence, entities=entities, entity_spans=tuple(spans))
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with self.assertRaises(ValueError):
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tokenizer(sentence, entities=[0], entity_spans=spans)
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with self.assertRaises(ValueError):
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tokenizer(sentence, entities=entities, entity_spans=[0])
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with self.assertRaises(ValueError):
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tokenizer(sentence, entities=entities, entity_spans=spans + [(0, 9)])
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def test_if_tokenize_entity_classification_raise_error_with_invalid_inputs(self):
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tokenizer = self.get_tokenizer(task="entity_classification")
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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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with self.assertRaises(ValueError):
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tokenizer(sentence, entity_spans=[])
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with self.assertRaises(ValueError):
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tokenizer(sentence, entity_spans=[span, span])
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with self.assertRaises(ValueError):
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tokenizer(sentence, entity_spans=[0])
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def test_if_tokenize_entity_pair_classification_raise_error_with_invalid_inputs(self):
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tokenizer = self.get_tokenizer(task="entity_pair_classification")
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sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
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# head and tail information
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with self.assertRaises(ValueError):
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tokenizer(sentence, entity_spans=[])
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with self.assertRaises(ValueError):
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tokenizer(sentence, entity_spans=[0, 0])
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def test_if_tokenize_entity_span_classification_raise_error_with_invalid_inputs(self):
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tokenizer = self.get_tokenizer(task="entity_span_classification")
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sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
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with self.assertRaises(ValueError):
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tokenizer(sentence, entity_spans=[])
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with self.assertRaises(ValueError):
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tokenizer(sentence, entity_spans=[0, 0, 0])
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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"
|
|
sentence_pair = "She could hardly believe her luck."
|
|
spans = [(9, 21), (30, 38)]
|
|
spans_pair = [(0, 3)]
|
|
|
|
encoding = tokenizer(
|
|
sentence,
|
|
sentence_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),
|
|
"<s>Top seed Ana Ivanovic said on Thursday</s></s>She could hardly believe her luck.</s>",
|
|
)
|
|
self.assertEqual(
|
|
tokenizer.decode(encoding["input_ids"][3:6], spaces_between_special_tokens=False), " Ana Ivanovic"
|
|
)
|
|
self.assertEqual(
|
|
tokenizer.decode(encoding["input_ids"][8:9], spaces_between_special_tokens=False), " Thursday"
|
|
)
|
|
self.assertEqual(tokenizer.decode(encoding["input_ids"][11:12], spaces_between_special_tokens=False), "She")
|
|
|
|
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"],
|
|
[
|
|
[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],
|
|
[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],
|
|
[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],
|
|
]
|
|
)
|
|
# fmt: on
|
|
|
|
def test_text_pair_padding_pytorch_tensors(self):
|
|
tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", return_token_type_ids=True)
|
|
sentence = "Top seed Ana Ivanovic said on Thursday"
|
|
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))
|