paddlenlp.transformers.bert.tokenizer 源代码

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import copy
import os
import unicodedata

from .. import PretrainedTokenizer, AddedToken
from ..tokenizer_utils import convert_to_unicode, whitespace_tokenize, _is_whitespace, _is_control, _is_punctuation

__all__ = [

[文档]class BasicTokenizer(object): """ Runs basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (bool): Whether or not to lowercase the input when tokenizing. Defaults to `True`. """ def __init__(self, do_lower_case=True): """Constructs a BasicTokenizer.""" self.do_lower_case = do_lower_case
[文档] def tokenize(self, text): """ Tokenizes a piece of text using basic tokenizer. Args: text (str): A piece of text. Returns: list(str): A list of tokens. Examples: .. code-block:: from paddlenlp.transformers import BasicTokenizer basictokenizer = BasicTokenizer() tokens = basictokenizer.tokenize('He was a puppeteer') ''' ['he', 'was', 'a', 'puppeteer'] ''' """ text = convert_to_unicode(text) text = self._clean_text(text) text = self._tokenize_chinese_chars(text) orig_tokens = whitespace_tokenize(text) split_tokens = [] for token in orig_tokens: if self.do_lower_case: token = token.lower() token = self._run_strip_accents(token) else: token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token)) 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): """ Splits punctuation on a piece of 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: # # # 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)
[文档]class WordpieceTokenizer(object): """ Runs WordPiece tokenization. Args: vocab (Vocab|dict): Vocab of the word piece tokenizer. unk_token (str): A specific token to replace all unknown tokens. max_input_chars_per_word (int): If a word's length is more than max_input_chars_per_word, it will be dealt as unknown word. Defaults to 100. """ 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
[文档] 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. Args: text: A single token or whitespace separated tokens. This should have already been passed through `BasicTokenizer`. Returns: list (str): A list of wordpiece tokens. Examples: .. code-block:: from paddlenlp.transformers import BertTokenizer, WordpieceTokenizer berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased') vocab = berttokenizer.vocab unk_token = berttokenizer.unk_token wordpiecetokenizer = WordpieceTokenizer(vocab,unk_token) inputs = wordpiecetokenizer.tokenize("unaffable") print(inputs) ''' ["un", "##aff", "##able"] ''' """ 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
[文档]class BertTokenizer(PretrainedTokenizer): """ Constructs a BERT tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords. Args: vocab_file (str): The vocabulary file path (ends with '.txt') required to instantiate a `WordpieceTokenizer`. do_lower_case (bool): Whether or not to lowercase the input when tokenizing. Defaults to`True`. unk_token (str): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to "[UNK]". sep_token (str): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str): A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to "[CLS]". mask_token (str): A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to "[MASK]". Examples: .. code-block:: from paddlenlp.transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') inputs = tokenizer('He was a puppeteer') print(inputs) ''' {'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0]} ''' """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "bert-base-uncased": "", "bert-large-uncased": "", "bert-base-cased": "", "bert-large-cased": "", "bert-base-multilingual-uncased": "", "bert-base-multilingual-cased": "", "bert-base-chinese": "", "bert-wwm-chinese": "", "bert-wwm-ext-chinese": "", "macbert-large-chinese": "", "macbert-base-chinese": "", "simbert-base-chinese": "", } } pretrained_init_configuration = { "bert-base-uncased": { "do_lower_case": True }, "bert-large-uncased": { "do_lower_case": True }, "bert-base-cased": { "do_lower_case": False }, "bert-large-cased": { "do_lower_case": False }, "bert-base-multilingual-uncased": { "do_lower_case": True }, "bert-base-multilingual-cased": { "do_lower_case": False }, "bert-base-chinese": { "do_lower_case": False }, "bert-wwm-chinese": { "do_lower_case": False }, "bert-wwm-ext-chinese": { "do_lower_case": False }, "macbert-large-chinese": { "do_lower_case": False }, "macbert-base-chinese": { "do_lower_case": False }, "simbert-base-chinese": { "do_lower_case": True }, } padding_side = 'right' def __init__(self, vocab_file, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.do_lower_case = do_lower_case self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer( vocab=self.vocab, unk_token=unk_token) @property def vocab_size(self): """ Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab) def _tokenize(self, text): """ End-to-end tokenization for BERT models. Args: text (str): The text to be tokenized. Returns: list: A list of string representing converted tokens. """ split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) return split_tokens
[文档] def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (list of string) to a single string. Since the usage of WordPiece introducing `##` to concat subwords, also removes `##` when converting. Args: tokens (list): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. Examples: .. code-block:: from paddlenlp.transformers import BertTokenizer berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased') tokens = berttokenizer.tokenize('He was a puppeteer') ''' ['he', 'was', 'a', 'puppet', '##eer'] ''' strings = tokenizer.convert_tokens_to_string(tokens) ''' he was a puppeteer ''' """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[文档] def num_special_tokens_to_add(self, pair=False): """ Returns the number of added tokens when encoding a sequence with special tokens. Args: pair(bool): Whether the input is a sequence pair or a single sequence. Defaults to `False` and the input is a single sequence. Returns: int: Number of tokens added to sequences. """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` Args: token_ids_0 (List[int]): List of IDs to which the special tokens will be added. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to None. Returns: List[int]: List of input_id with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. A BERT offset_mapping has the following format: - single sequence: ``(0,0) X (0,0)`` - pair of sequences: ``(0,0) A (0,0) B (0,0)`` Args: offset_mapping_ids_0 (List[tuple]): List of wordpiece offsets to which the special tokens will be added. offset_mapping_ids_1 (List[tuple], optional): Optional second list of wordpiece offsets for offset mapping pairs. Defaults to None. Returns: List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return [(0, 0)] + offset_mapping_0 + [(0, 0)] return [(0, 0)] + offset_mapping_0 + [(0, 0) ] + offset_mapping_1 + [(0, 0)]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to None. Returns: List[int]: List of token_type_id according to the given sequence(s). """ _sep = [self.sep_token_id] _cls = [self.cls_token_id] if token_ids_1 is None: return len(_cls + token_ids_0 + _sep) * [0] return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optinal): Optional second list of IDs for sequence pairs. Defaults to None. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to None. Returns: List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formatted with special tokens for the model." ) return list( map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ( [0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1]