501 lines
20 KiB
Python
501 lines
20 KiB
Python
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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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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"""Tokenization classes for Bert."""
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import collections
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import os
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import unicodedata
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from typing import List, Optional, Tuple
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from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
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def load_vocab(vocab_file):
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"""Loads a vocabulary file into a dictionary."""
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vocab = collections.OrderedDict()
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with open(vocab_file, "r", encoding="utf-8") as reader:
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tokens = reader.readlines()
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for index, token in enumerate(tokens):
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token = token.rstrip("\n")
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vocab[token] = index
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return vocab
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
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text = text.strip()
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if not text:
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return []
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tokens = text.split()
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return tokens
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class BertTokenizer(PreTrainedTokenizer):
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r"""
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Construct a BERT tokenizer. Based on WordPiece.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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File containing the vocabulary.
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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do_basic_tokenize (`bool`, *optional*, defaults to `True`):
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Whether or not to do basic tokenization before WordPiece.
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never_split (`Iterable`, *optional*):
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Collection of tokens which will never be split during tokenization. Only has an effect when
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`do_basic_tokenize=True`
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unk_token (`str`, *optional*, defaults to `"[UNK]"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"[PAD]"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (`str`, *optional*, defaults to `"[CLS]"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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mask_token (`str`, *optional*, defaults to `"[MASK]"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
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Whether or not to tokenize Chinese characters.
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This should likely be deactivated for Japanese (see this
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[issue](https://github.com/huggingface/transformers/issues/328)).
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strip_accents (`bool`, *optional*):
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Whether or not to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original BERT).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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do_lower_case=True,
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do_basic_tokenize=True,
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never_split=None,
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unk_token="[UNK]",
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sep_token="[SEP]",
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pad_token="[PAD]",
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cls_token="[CLS]",
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mask_token="[MASK]",
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tokenize_chinese_chars=True,
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strip_accents=None,
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**kwargs,
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):
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if not os.path.isfile(vocab_file):
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raise ValueError(
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f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
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" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
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)
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self.vocab = load_vocab(vocab_file)
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self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
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self.do_basic_tokenize = do_basic_tokenize
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if do_basic_tokenize:
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self.basic_tokenizer = BasicTokenizer(
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do_lower_case=do_lower_case,
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never_split=never_split,
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tokenize_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
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super().__init__(
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do_lower_case=do_lower_case,
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do_basic_tokenize=do_basic_tokenize,
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never_split=never_split,
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unk_token=unk_token,
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sep_token=sep_token,
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pad_token=pad_token,
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cls_token=cls_token,
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mask_token=mask_token,
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tokenize_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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**kwargs,
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)
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@property
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def do_lower_case(self):
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return self.basic_tokenizer.do_lower_case
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@property
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def vocab_size(self):
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return len(self.vocab)
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def get_vocab(self):
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return dict(self.vocab, **self.added_tokens_encoder)
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def _tokenize(self, text, split_special_tokens=False):
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split_tokens = []
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if self.do_basic_tokenize:
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for token in self.basic_tokenizer.tokenize(
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text, never_split=self.all_special_tokens if not split_special_tokens else None
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):
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# If the token is part of the never_split set
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if token in self.basic_tokenizer.never_split:
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split_tokens.append(token)
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else:
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split_tokens += self.wordpiece_tokenizer.tokenize(token)
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else:
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split_tokens = self.wordpiece_tokenizer.tokenize(text)
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return split_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.vocab.get(token, self.vocab.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.ids_to_tokens.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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out_string = " ".join(tokens).replace(" ##", "").strip()
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return out_string
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A BERT sequence has the following format:
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- single sequence: `[CLS] X [SEP]`
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- pair of sequences: `[CLS] A [SEP] B [SEP]`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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cls = [self.cls_token_id]
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sep = [self.sep_token_id]
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return cls + token_ids_0 + sep + token_ids_1 + sep
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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if token_ids_1 is not None:
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1]
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
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pair mask has the following format:
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```
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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```
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
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"""
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sep = [self.sep_token_id]
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cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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index = 0
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if os.path.isdir(save_directory):
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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else:
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vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
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with open(vocab_file, "w", encoding="utf-8") as writer:
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for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
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" Please check that the vocabulary is not corrupted!"
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)
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index = token_index
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writer.write(token + "\n")
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index += 1
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return (vocab_file,)
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class BasicTokenizer(object):
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"""
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Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
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Args:
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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never_split (`Iterable`, *optional*):
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Collection of tokens which will never be split during tokenization. Only has an effect when
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`do_basic_tokenize=True`
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tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
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Whether or not to tokenize Chinese characters.
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This should likely be deactivated for Japanese (see this
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[issue](https://github.com/huggingface/transformers/issues/328)).
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strip_accents (`bool`, *optional*):
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Whether or not to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original BERT).
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do_split_on_punc (`bool`, *optional*, defaults to `True`):
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In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
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the full context of the words, such as contractions.
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"""
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def __init__(
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self,
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do_lower_case=True,
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never_split=None,
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tokenize_chinese_chars=True,
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strip_accents=None,
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do_split_on_punc=True,
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):
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if never_split is None:
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never_split = []
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self.do_lower_case = do_lower_case
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self.never_split = set(never_split)
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self.tokenize_chinese_chars = tokenize_chinese_chars
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self.strip_accents = strip_accents
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self.do_split_on_punc = do_split_on_punc
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def tokenize(self, text, never_split=None):
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"""
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Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
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Args:
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never_split (`List[str]`, *optional*)
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Kept for backward compatibility purposes. Now implemented directly at the base class level (see
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[`PreTrainedTokenizer.tokenize`]) List of token not to split.
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"""
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# union() returns a new set by concatenating the two sets.
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never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
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text = self._clean_text(text)
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# This was added on November 1st, 2018 for the multilingual and Chinese
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# models. This is also applied to the English models now, but it doesn't
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# matter since the English models were not trained on any Chinese data
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# and generally don't have any Chinese data in them (there are Chinese
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# characters in the vocabulary because Wikipedia does have some Chinese
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# words in the English Wikipedia.).
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if self.tokenize_chinese_chars:
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text = self._tokenize_chinese_chars(text)
|
||
|
|
# prevents treating the same character with different unicode codepoints as different characters
|
||
|
|
unicode_normalized_text = unicodedata.normalize("NFC", text)
|
||
|
|
orig_tokens = whitespace_tokenize(unicode_normalized_text)
|
||
|
|
split_tokens = []
|
||
|
|
for token in orig_tokens:
|
||
|
|
if token not in never_split:
|
||
|
|
if self.do_lower_case:
|
||
|
|
token = token.lower()
|
||
|
|
if self.strip_accents is not False:
|
||
|
|
token = self._run_strip_accents(token)
|
||
|
|
elif self.strip_accents:
|
||
|
|
token = self._run_strip_accents(token)
|
||
|
|
split_tokens.extend(self._run_split_on_punc(token, never_split))
|
||
|
|
|
||
|
|
output_tokens = whitespace_tokenize(" ".join(split_tokens))
|
||
|
|
return output_tokens
|
||
|
|
|
||
|
|
def _run_strip_accents(self, text):
|
||
|
|
"""Strips accents from a piece of text."""
|
||
|
|
text = unicodedata.normalize("NFD", text)
|
||
|
|
output = []
|
||
|
|
for char in text:
|
||
|
|
cat = unicodedata.category(char)
|
||
|
|
if cat == "Mn":
|
||
|
|
continue
|
||
|
|
output.append(char)
|
||
|
|
return "".join(output)
|
||
|
|
|
||
|
|
def _run_split_on_punc(self, text, never_split=None):
|
||
|
|
"""Splits punctuation on a piece of text."""
|
||
|
|
if not self.do_split_on_punc or (never_split is not None and text in never_split):
|
||
|
|
return [text]
|
||
|
|
chars = list(text)
|
||
|
|
i = 0
|
||
|
|
start_new_word = True
|
||
|
|
output = []
|
||
|
|
while i < len(chars):
|
||
|
|
char = chars[i]
|
||
|
|
if _is_punctuation(char):
|
||
|
|
output.append([char])
|
||
|
|
start_new_word = True
|
||
|
|
else:
|
||
|
|
if start_new_word:
|
||
|
|
output.append([])
|
||
|
|
start_new_word = False
|
||
|
|
output[-1].append(char)
|
||
|
|
i += 1
|
||
|
|
|
||
|
|
return ["".join(x) for x in output]
|
||
|
|
|
||
|
|
def _tokenize_chinese_chars(self, text):
|
||
|
|
"""Adds whitespace around any CJK character."""
|
||
|
|
output = []
|
||
|
|
for char in text:
|
||
|
|
cp = ord(char)
|
||
|
|
if self._is_chinese_char(cp):
|
||
|
|
output.append(" ")
|
||
|
|
output.append(char)
|
||
|
|
output.append(" ")
|
||
|
|
else:
|
||
|
|
output.append(char)
|
||
|
|
return "".join(output)
|
||
|
|
|
||
|
|
def _is_chinese_char(self, cp):
|
||
|
|
"""Checks whether CP is the codepoint of a CJK character."""
|
||
|
|
# This defines a "chinese character" as anything in the CJK Unicode block:
|
||
|
|
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
|
||
|
|
#
|
||
|
|
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
|
||
|
|
# despite its name. The modern Korean Hangul alphabet is a different block,
|
||
|
|
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
|
||
|
|
# space-separated words, so they are not treated specially and handled
|
||
|
|
# like the all of the other languages.
|
||
|
|
if (
|
||
|
|
(cp >= 0x4E00 and cp <= 0x9FFF)
|
||
|
|
or (cp >= 0x3400 and cp <= 0x4DBF) #
|
||
|
|
or (cp >= 0x20000 and cp <= 0x2A6DF) #
|
||
|
|
or (cp >= 0x2A700 and cp <= 0x2B73F) #
|
||
|
|
or (cp >= 0x2B740 and cp <= 0x2B81F) #
|
||
|
|
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
|
||
|
|
or (cp >= 0xF900 and cp <= 0xFAFF)
|
||
|
|
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
|
||
|
|
): #
|
||
|
|
return True
|
||
|
|
|
||
|
|
return False
|
||
|
|
|
||
|
|
def _clean_text(self, text):
|
||
|
|
"""Performs invalid character removal and whitespace cleanup on text."""
|
||
|
|
output = []
|
||
|
|
for char in text:
|
||
|
|
cp = ord(char)
|
||
|
|
if cp == 0 or cp == 0xFFFD or _is_control(char):
|
||
|
|
continue
|
||
|
|
if _is_whitespace(char):
|
||
|
|
output.append(" ")
|
||
|
|
else:
|
||
|
|
output.append(char)
|
||
|
|
return "".join(output)
|
||
|
|
|
||
|
|
|
||
|
|
class WordpieceTokenizer(object):
|
||
|
|
"""Runs WordPiece tokenization."""
|
||
|
|
|
||
|
|
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
|
||
|
|
self.vocab = vocab
|
||
|
|
self.unk_token = unk_token
|
||
|
|
self.max_input_chars_per_word = max_input_chars_per_word
|
||
|
|
|
||
|
|
def tokenize(self, text):
|
||
|
|
"""
|
||
|
|
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
|
||
|
|
tokenization using the given vocabulary.
|
||
|
|
|
||
|
|
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text: A single token or whitespace separated tokens. This should have
|
||
|
|
already been passed through *BasicTokenizer*.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A list of wordpiece tokens.
|
||
|
|
"""
|
||
|
|
|
||
|
|
output_tokens = []
|
||
|
|
for token in whitespace_tokenize(text):
|
||
|
|
chars = list(token)
|
||
|
|
if len(chars) > self.max_input_chars_per_word:
|
||
|
|
output_tokens.append(self.unk_token)
|
||
|
|
continue
|
||
|
|
|
||
|
|
is_bad = False
|
||
|
|
start = 0
|
||
|
|
sub_tokens = []
|
||
|
|
while start < len(chars):
|
||
|
|
end = len(chars)
|
||
|
|
cur_substr = None
|
||
|
|
while start < end:
|
||
|
|
substr = "".join(chars[start:end])
|
||
|
|
if start > 0:
|
||
|
|
substr = "##" + substr
|
||
|
|
if substr in self.vocab:
|
||
|
|
cur_substr = substr
|
||
|
|
break
|
||
|
|
end -= 1
|
||
|
|
if cur_substr is None:
|
||
|
|
is_bad = True
|
||
|
|
break
|
||
|
|
sub_tokens.append(cur_substr)
|
||
|
|
start = end
|
||
|
|
|
||
|
|
if is_bad:
|
||
|
|
output_tokens.append(self.unk_token)
|
||
|
|
else:
|
||
|
|
output_tokens.extend(sub_tokens)
|
||
|
|
return output_tokens
|