paddlenlp.transformers.ernie_gram.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.

# 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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from ..ernie.tokenizer import ErnieTokenizer

__all__ = ["ErnieGramTokenizer"]

[文档]class ErnieGramTokenizer(ErnieTokenizer): r""" Constructs an ERNIE-Gram tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords. This tokenizer inherits from :class:`~paddlenlp.transformers.ernie.tokenizer.ErnieTokenizer`. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): The vocabulary file path (ends with '.txt') required to instantiate a `WordpieceTokenizer`. 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]". Examples: .. code-block:: from paddlenlp.transformers import ErnieGramTokenizer tokenizer = ErnieGramTokenizer.from_pretrained('ernie-gram-zh') encoded_inputs = tokenizer('He was a puppeteer') # encoded_inputs: # { # 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2], # 'token_type_ids': [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-gram-zh": "", "ernie-gram-zh-finetuned-dureader-robust": "", } } pretrained_init_configuration = { "ernie-gram-zh": {"do_lower_case": True}, "ernie-gram-zh-finetuned-dureader-robust": {"do_lower_case": True}, } max_model_input_sizes = { "ernie-gram-zh": 512, "ernie-gram-zh-finetuned-dureader-robust": 512, } 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 ): super(ErnieGramTokenizer, self).__init__( vocab_file, do_lower_case=do_lower_case, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, **kwargs, )