Source code for transformers.models.bert_japanese.tokenization_bert_japanese

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""Tokenization classes."""


import collections
import copy
import os
import unicodedata
from typing import Optional

from ...utils import logging
from ..bert.tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer, load_vocab


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/vocab.txt",
        "cl-tohoku/bert-base-japanese-whole-word-masking": "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/vocab.txt",
        "cl-tohoku/bert-base-japanese-char": "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/vocab.txt",
        "cl-tohoku/bert-base-japanese-char-whole-word-masking": "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/vocab.txt",
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "cl-tohoku/bert-base-japanese": 512,
    "cl-tohoku/bert-base-japanese-whole-word-masking": 512,
    "cl-tohoku/bert-base-japanese-char": 512,
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": 512,
}

PRETRAINED_INIT_CONFIGURATION = {
    "cl-tohoku/bert-base-japanese": {
        "do_lower_case": False,
        "word_tokenizer_type": "mecab",
        "subword_tokenizer_type": "wordpiece",
    },
    "cl-tohoku/bert-base-japanese-whole-word-masking": {
        "do_lower_case": False,
        "word_tokenizer_type": "mecab",
        "subword_tokenizer_type": "wordpiece",
    },
    "cl-tohoku/bert-base-japanese-char": {
        "do_lower_case": False,
        "word_tokenizer_type": "mecab",
        "subword_tokenizer_type": "character",
    },
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": {
        "do_lower_case": False,
        "word_tokenizer_type": "mecab",
        "subword_tokenizer_type": "character",
    },
}


[docs]class BertJapaneseTokenizer(BertTokenizer): r""" Construct a BERT tokenizer for Japanese text, based on a MecabTokenizer. Args: vocab_file (:obj:`str`): Path to a one-wordpiece-per-line vocabulary file. do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to lower case the input. Only has an effect when do_basic_tokenize=True. do_word_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to do word tokenization. do_subword_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to do subword tokenization. word_tokenizer_type (:obj:`str`, `optional`, defaults to :obj:`"basic"`): Type of word tokenizer. subword_tokenizer_type (:obj:`str`, `optional`, defaults to :obj:`"wordpiece"`): Type of subword tokenizer. mecab_kwargs (:obj:`str`, `optional`): Dictionary passed to the :obj:`MecabTokenizer` constructor. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, do_lower_case=False, do_word_tokenize=True, do_subword_tokenize=True, word_tokenizer_type="basic", subword_tokenizer_type="wordpiece", never_split=None, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", mecab_kwargs=None, **kwargs ): super(BertTokenizer, self).__init__( unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, do_lower_case=do_lower_case, do_word_tokenize=do_word_tokenize, do_subword_tokenize=do_subword_tokenize, word_tokenizer_type=word_tokenizer_type, subword_tokenizer_type=subword_tokenizer_type, never_split=never_split, mecab_kwargs=mecab_kwargs, **kwargs, ) # ^^ We call the grandparent's init, not the parent's. if not os.path.isfile(vocab_file): raise ValueError( f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained " "model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" ) self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) self.do_word_tokenize = do_word_tokenize self.word_tokenizer_type = word_tokenizer_type self.lower_case = do_lower_case self.never_split = never_split self.mecab_kwargs = copy.deepcopy(mecab_kwargs) if do_word_tokenize: if word_tokenizer_type == "basic": self.word_tokenizer = BasicTokenizer( do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=False ) elif word_tokenizer_type == "mecab": self.word_tokenizer = MecabTokenizer( do_lower_case=do_lower_case, never_split=never_split, **(mecab_kwargs or {}) ) else: raise ValueError(f"Invalid word_tokenizer_type '{word_tokenizer_type}' is specified.") self.do_subword_tokenize = do_subword_tokenize self.subword_tokenizer_type = subword_tokenizer_type if do_subword_tokenize: if subword_tokenizer_type == "wordpiece": self.subword_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token) elif subword_tokenizer_type == "character": self.subword_tokenizer = CharacterTokenizer(vocab=self.vocab, unk_token=self.unk_token) else: raise ValueError(f"Invalid subword_tokenizer_type '{subword_tokenizer_type}' is specified.") @property def do_lower_case(self): return self.lower_case def __getstate__(self): state = dict(self.__dict__) if self.word_tokenizer_type == "mecab": del state["word_tokenizer"] return state def __setstate__(self, state): self.__dict__ = state if self.word_tokenizer_type == "mecab": self.word_tokenizer = MecabTokenizer( do_lower_case=self.do_lower_case, never_split=self.never_split, **(self.mecab_kwargs or {}) ) def _tokenize(self, text): if self.do_word_tokenize: tokens = self.word_tokenizer.tokenize(text, never_split=self.all_special_tokens) else: tokens = [text] if self.do_subword_tokenize: split_tokens = [sub_token for token in tokens for sub_token in self.subword_tokenizer.tokenize(token)] else: split_tokens = tokens return split_tokens
class MecabTokenizer: """Runs basic tokenization with MeCab morphological parser.""" def __init__( self, do_lower_case=False, never_split=None, normalize_text=True, mecab_dic: Optional[str] = "ipadic", mecab_option: Optional[str] = None, ): """ Constructs a MecabTokenizer. Args: **do_lower_case**: (`optional`) boolean (default True) Whether to lowercase the input. **never_split**: (`optional`) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`) List of tokens not to split. **normalize_text**: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization. **mecab_dic**: (`optional`) string (default "ipadic") Name of dictionary to be used for MeCab initialization. If you are using a system-installed dictionary, set this option to `None` and modify `mecab_option`. **mecab_option**: (`optional`) string String passed to MeCab constructor. """ self.do_lower_case = do_lower_case self.never_split = never_split if never_split is not None else [] self.normalize_text = normalize_text try: import fugashi except ModuleNotFoundError as error: raise error.__class__( "You need to install fugashi to use MecabTokenizer. " "See https://pypi.org/project/fugashi/ for installation." ) mecab_option = mecab_option or "" if mecab_dic is not None: if mecab_dic == "ipadic": try: import ipadic except ModuleNotFoundError as error: raise error.__class__( "The ipadic dictionary is not installed. " "See https://github.com/polm/ipadic-py for installation." ) dic_dir = ipadic.DICDIR elif mecab_dic == "unidic_lite": try: import unidic_lite except ModuleNotFoundError as error: raise error.__class__( "The unidic_lite dictionary is not installed. " "See https://github.com/polm/unidic-lite for installation." ) dic_dir = unidic_lite.DICDIR elif mecab_dic == "unidic": try: import unidic except ModuleNotFoundError as error: raise error.__class__( "The unidic dictionary is not installed. " "See https://github.com/polm/unidic-py for installation." ) dic_dir = unidic.DICDIR if not os.path.isdir(dic_dir): raise RuntimeError( "The unidic dictionary itself is not found." "See https://github.com/polm/unidic-py for installation." ) else: raise ValueError("Invalid mecab_dic is specified.") mecabrc = os.path.join(dic_dir, "mecabrc") mecab_option = f'-d "{dic_dir}" -r "{mecabrc}" ' + mecab_option self.mecab = fugashi.GenericTagger(mecab_option) def tokenize(self, text, never_split=None, **kwargs): """Tokenizes a piece of text.""" if self.normalize_text: text = unicodedata.normalize("NFKC", text) never_split = self.never_split + (never_split if never_split is not None else []) tokens = [] for word in self.mecab(text): token = word.surface if self.do_lower_case and token not in never_split: token = token.lower() tokens.append(token) return tokens class CharacterTokenizer: """Runs Character tokenization.""" def __init__(self, vocab, unk_token, normalize_text=True): """ Constructs a CharacterTokenizer. Args: **vocab**: Vocabulary object. **unk_token**: str A special symbol for out-of-vocabulary token. **normalize_text**: (`optional`) boolean (default True) Whether to apply unicode normalization to text before tokenization. """ self.vocab = vocab self.unk_token = unk_token self.normalize_text = normalize_text def tokenize(self, text): """ Tokenizes a piece of text into characters. For example, :obj:`input = "apple""` wil return as output :obj:`["a", "p", "p", "l", "e"]`. Args: text: A single token or whitespace separated tokens. This should have already been passed through `BasicTokenizer`. Returns: A list of characters. """ if self.normalize_text: text = unicodedata.normalize("NFKC", text) output_tokens = [] for char in text: if char not in self.vocab: output_tokens.append(self.unk_token) continue output_tokens.append(char) return output_tokens