paddlenlp.transformers.ernie_m.tokenizer 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.

# 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 os

import sentencepiece as spm
import unicodedata
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union

from ..tokenizer_utils_base import TensorType, PaddingStrategy, TruncationStrategy
from .. import PretrainedTokenizer

__all__ = ['ErnieMTokenizer']


[文档]class ErnieMTokenizer(PretrainedTokenizer): r""" Constructs a ErnieM tokenizer. It uses the `sentencepiece` tools to cut the words to sub-words. Args: vocab_file (str): The file path of the vocabulary. sentencepiece_model_file (str): The file path of sentencepiece model. do_lower_case (str, optional): Whether or not to lowercase the input when tokenizing. Defaults to`True`. unk_token (str, optional): 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, optional): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str, optional): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str, optional): 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, optional): 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]". """ resource_files_names = { "sentencepiece_model_file": "sentencepiece.bpe.model", "vocab_file": "vocab.txt", } # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "ernie-m-base": "", "ernie-m-large": "" }, "sentencepiece_model_file": { "ernie-m-base": "", "ernie-m-large": "" } } pretrained_init_configuration = { "ernie-m-base": { "do_lower_case": True }, "ernie-m-large": { "do_lower_case": True } } def __init__(self, vocab_file, sentencepiece_model_file, do_lower_case=True, encoding="utf8", unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): self.sp_model = spm.SentencePieceProcessor() self.do_lower_case = do_lower_case self.encoding = encoding if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'.".format(vocab_file)) self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) self.vocab_file = vocab_file self.sentencepiece_model_file = sentencepiece_model_file if os.path.isfile(sentencepiece_model_file): self.sp_model.Load(sentencepiece_model_file) self.SP_CHAR_MAPPING = {} for ch in range(65281, 65375): if ch in [ord(u'~')]: self.SP_CHAR_MAPPING[chr(ch)] = chr(ch) continue self.SP_CHAR_MAPPING[chr(ch)] = chr(ch - 65248) def __call__(self, text: Union[str, List[str], List[List[str]]], text_pair: Optional[Union[str, List[str], List[List[str]]]] = None, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: bool = False, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = False, return_position_ids: bool = True, return_token_type_ids: bool = False, return_attention_mask: bool = True, return_length: bool = False, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_dict: bool = True, return_offsets_mapping: bool = False, add_special_tokens: bool = True, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, **kwargs): return super(ErnieMTokenizer, self).__call__( text=text, text_pair=text_pair, max_length=max_length, stride=stride, is_split_into_words=is_split_into_words, padding=padding, truncation=truncation, return_position_ids=return_position_ids, # Ernie-M model doesn't have token_type embedding. # So set "return_token_type_ids" to False. return_token_type_ids=False, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_dict=return_dict, return_offsets_mapping=return_offsets_mapping, add_special_tokens=add_special_tokens, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, verbose=verbose, **kwargs)
[文档] def get_offset_mapping(self, text): split_tokens = self._tokenize(text) normalized_text, char_mapping = '', [] for i, ch in enumerate(text): if ch in self.SP_CHAR_MAPPING: ch = self.SP_CHAR_MAPPING.get(ch) else: ch = unicodedata.normalize('NFKC', ch) if self.is_whitespace(ch): continue normalized_text += ch char_mapping.extend([i] * len(ch)) text, token_mapping, offset = normalized_text, [], 0 for token in split_tokens: if token[:1] == '▁': token = token[1:] start = text[offset:].index(token) + offset end = start + len(token) token_mapping.append( (char_mapping[start], char_mapping[end - 1] + 1)) offset = end return token_mapping
@property def vocab_size(self): r""" Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab)
[文档] def clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" return ''.join((self.SP_CHAR_MAPPING.get(c, c) for c in text))
def _tokenize(self, text, sample=False): """Tokenize a string.""" if not sample: pieces = self.sp_model.EncodeAsPieces(text) else: pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1) new_pieces = [] for piece in pieces: if piece == SPIECE_UNDERLINE: continue lst_i = 0 for i, c in enumerate(piece): if c == SPIECE_UNDERLINE: continue if self.is_ch_char(c) or self.is_punct(c): if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE: new_pieces.append(piece[lst_i:i]) new_pieces.append(c) lst_i = i + 1 elif c.isdigit() and i > 0 and not piece[i - 1].isdigit(): if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE: new_pieces.append(piece[lst_i:i]) lst_i = i elif not c.isdigit() and i > 0 and piece[i - 1].isdigit(): if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE: new_pieces.append(piece[lst_i:i]) lst_i = i if len(piece) > lst_i: new_pieces.append(piece[lst_i:]) return new_pieces
[文档] def tokenize(self, text, **kwargs): r""" Converts a string to a list of tokens. Args: text (str): The text to be tokenized. Returns: List(str): A list of string representing converted tokens. """ return self._tokenize(text)
[文档] def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string
[文档] def convert_ids_to_string(self, ids): """ Converts a sequence of tokens (strings for sub-words) in a single string. """ tokens = self.convert_ids_to_tokens(ids) out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): r""" Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An ERNIE-M sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] [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 + _sep + token_ids_1 + _sep
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): r""" Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. An ERNIE-M offset_mapping has the following format: - single sequence: ``(0,0) X (0,0)`` - pair of sequences: ``(0,0) A (0,0) (0,0) B (0,0)`` Args: offset_mapping_ids_0 (List[tuple]): List of char 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]: 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), (0, 0) ] + offset_mapping_1 + [(0, 0)]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): r""" 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]): List of ids of the first sequence. token_ids_1 (List[int], optinal): Optional second list of IDs for sequence pairs. Defaults to `None`. already_has_special_tokens (str, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to `False`. Returns: List[int]: The list of integers in the range [0, 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, 1] + ( [0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1]
[文档] def is_ch_char(self, char): """ is_ch_char """ if u'\u4e00' <= char <= u'\u9fff': return True return False
[文档] def is_alpha(self, char): """ is_alpha """ if 'a' <= char <= 'z': return True if 'A' <= char <= 'Z': return True return False
[文档] def is_punct(self, char): """ is_punct """ if char in u",;:.?!~,;:。?!《》【】": return True return False
[文档] def is_whitespace(self, char): """ is whitespace """ if char == " " or char == "\t" or char == "\n" or char == "\r": return True if len(char) == 1: cat = unicodedata.category(char) if cat == "Zs": return True return False