paddlenlp.transformers.megatronbert.tokenizer 源代码

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# Copyright 2018 The HuggingFace Inc. team.
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"""Tokenization classes for MegatronBert."""

from .. import BertTokenizer

__all__ = ["MegatronBertTokenizer"]

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"megatronbert-cased": 512, "megatronbert-uncased": 512}

[文档]class MegatronBertTokenizer(BertTokenizer): """ Constructs a MegatronBert 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 MegatronBertTokenizer tokenizer = MegatronBertTokenizer.from_pretrained('MegatronBert-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": { "megatronbert-uncased": "", "megatronbert-cased": "", } } pretrained_init_configuration = { "megatronbert-uncased": {"do_lower_case": True}, "megatronbert-cased": {"do_lower_case": False}, } max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES 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(MegatronBertTokenizer, 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, )