paddlenlp.transformers.ernie_ctm.tokenizer 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.

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#     http://www.apache.org/licenses/LICENSE-2.0
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import os
import pickle
import six
import shutil

from paddle.utils import try_import
from paddlenlp.utils.env import MODEL_HOME

from .. import BasicTokenizer, PretrainedTokenizer

__all__ = ['ErnieCtmTokenizer']

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "ernie-ctm": 512,
    "wordtag": 512,
    "nptag": 512
}


[文档]class ErnieCtmTokenizer(PretrainedTokenizer): r""" Construct an ERNIE-CTM tokenizer. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): File path of the vocabulary. do_lower_case (bool, optional): Whether or not to lowercase the input when tokenizing. Defaults to `True` do_basic_tokenize (bool, optional): Whether or not to do basic tokenization before WordPiece. 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_template (str, optional) The template of summary token for multiple summary placeholders. Defaults to `"[CLS{}]"` cls_num (int, optional): Summary placeholder used in ernie-ctm model. For catching a sentence global feature from multiple aware. Defaults to `1`. mask_token (str, optional): A special token representing a masked token. This is the token used in the masked language modeling task. This is the token which the model will try to predict the original unmasked ones. Defaults to `"[MASK]"`. strip_accents: (bool, optional): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). Examples: .. code-block:: from paddlenlp.transformers import ErnieCtmTokenizer tokenizer = ErnieCtmTokenizer.from_pretrained('ernie-ctm') encoded_inputs = tokenizer('He was a puppeteer') # encoded_inputs: # {'input_ids': [101, 98, 153, 150, 99, 168, 146, 164, 99, 146, 99, 161, 166, 161, # 161, 150, 165, 150, 150, 163, 102], # 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "ernie-ctm": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_ctm/vocab.txt", "wordtag": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_ctm/vocab.txt", "nptag": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_ctm/vocab.txt", } } pretrained_init_configuration = { "ernie-ctm": { "do_lower_case": True, "cls_num": 2 }, "wordtag": { "do_lower_case": True, "cls_num": 2 }, "nptag": { "do_lower_case": True, "cls_num": 2 }, } max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token_template="[CLS{}]", cls_num=1, 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 = ErnieTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.do_lower_case = do_lower_case self.cls_token_template = cls_token_template self.cls_num = cls_num self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) @property def vocab_size(self): """ Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab)
[文档] def convert_tokens_to_string(self, tokens): r""" Converts a sequence of tokens (list of string) in a single string. Since the usage of WordPiece introducing `##` to concat subwords, also remove `##` when converting. Args: tokens (List[str]): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. Examples: .. code-block:: from paddlenlp.transformers import ErnieCtmTokenizer tokenizer = ErnieCtmTokenizer.from_pretrained('ernie-ctm') tokens = tokenizer.tokenize('He was a puppeteer') strings = tokenizer.convert_tokens_to_string(tokens) #he was a puppeteer """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[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 sequences for sequence classification tasks by concatenating and add special tokens. A ERNIE-CTM sequence has the following format: - single sequence: [CLS0][CLS1]... X [SEP] - pair of sequences: [CLS0][CLS1]... X [SEP] X [SEP] Args: token_ids_0 (List): List of IDs to which the special tokens will be added. token_ids_1 (List, optional): Optional second list of IDs for sequence pairs. Defaults to ``None``. Returns: List[int]: The input_id with the appropriate special tokens. """ cls_token_ids = [ self.convert_tokens_to_ids(self.cls_token_template.format(sid)) for sid in range(self.cls_num) ] if token_ids_1 is None: return cls_token_ids + token_ids_0 + [self.sep_token_id] return cls_token_ids + token_ids_0 + [ self.sep_token_id ] + token_ids_1 + [self.sep_token_id]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Creates a special tokens mask from the input sequences. This method is called when adding special tokens using the tokenizer `encode` method. 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 `inputs_ids` for the second sequence. Defaults to `None`. already_has_special_tokens (bool, optional): Whether or not the token list already contains special tokens for the model. Defaults to `False`. Returns: List[int]: A list of integers which is either 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]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Creates a token_type mask from the input sequences. If `token_ids_1` is not `None`, then a sequence pair token_type 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 2 | first sequence | second sequence | Else if `token_ids_1` is `None`, then a single sequence token_type mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 | first sequence | - 0 stands for the segment id of **first segment tokens**, - 1 stands for the segment id of **second segment tokens**, - 2 stands for the segment id of **cls_token**. 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 `inputs_ids` for the second sequence. Defaults to `None`. Returns: List[int]: List of token type IDs according to the given sequence(s). """ sep = [self.sep_token_id] if token_ids_1 is None: return (self.cls_num + len(token_ids_0 + sep)) * [0] return (self.cls_num + len(token_ids_0 + sep)) * [0] + len(token_ids_1 + sep) * [1]
[文档] def num_special_tokens_to_add(self, pair=False): """ Returns the number of added tokens when encoding a sequence with special tokens. Note: This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop. Args: pair (bool, optional): 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. """ if pair is True: return self.cls_num + 2 else: return self.cls_num + 1
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. """ orig_tokens = list(text) output_tokens = [] for token in orig_tokens: if self.do_lower_case is True: token = token.lower() output_tokens.append(token) return output_tokens