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Tokenizer accepts token list as well as string

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LysandreJik 2019-09-19 14:47:52 +02:00
parent 66ea76b8a9
commit 3df208c93a
2 changed files with 14 additions and 7 deletions
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7
pytorch_transformers/tests/tokenization_tests_commons.py
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@ -263,3 +263,10 @@ class CommonTestCases:
assert overflowing_tokens_first_truncated == sequence_0_no_special_tokens[-(2 + stride):]
assert len(truncated_sequence) == len(sequence) - 2
assert truncated_sequence == truncated_second_sequence
def test_tokens_sent_to_encode(self):
tokenizer = self.get_tokenizer()
sequence = "Let's encode this sequence"
tokens = tokenizer.encode(sequence)
tokenizer.encode(tokens, add_special_tokens=True)

14
pytorch_transformers/tokenization_utils.py
View File

@ -707,14 +707,14 @@ class PreTrainedTokenizer(object):
"""
if text_pair is None:
if add_special_tokens:
sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs)) if isinstance(text, str) else text
return self.add_special_tokens_single_sequence(sequence_tokens)
else:
ids = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
ids = self.convert_tokens_to_ids(self.tokenize(text, **kwargs)) if isinstance(text, str) else text
return ids
first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)] if isinstance(text, str) else text
second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)] if isinstance(text_pair, str) else text_pair
if add_special_tokens:
return self.add_special_tokens_sequence_pair(first_sentence_tokens, second_sentence_tokens)
@ -754,7 +754,7 @@ class PreTrainedTokenizer(object):
information = {}
if text_pair is None:
sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
sequence_tokens = self.convert_tokens_to_ids(self.tokenize(text, **kwargs)) if isinstance(text, str) else text
if add_special_tokens:
information = self.prepare_for_model(sequence_tokens, max_length, stride)
else:
@ -766,8 +766,8 @@ class PreTrainedTokenizer(object):
if output_mask:
information["mask"] = [0] * len(information["sequence"])
else:
first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)] if isinstance(text, str) else text
second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)] if isinstance(text_pair, str) else text_pair
if add_special_tokens:
information = self.prepare_pair_for_model(