paddlenlp.transformers.ernie_doc.tokenizer 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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import os
import pickle
import shutil
import json

from paddlenlp.utils.env import MODEL_HOME
from .. import PretrainedTokenizer, BPETokenizer
from ..ernie.tokenizer import ErnieTokenizer

__all__ = ['ErnieDocTokenizer', 'ErnieDocBPETokenizer']


[文档]class ErnieDocTokenizer(ErnieTokenizer): r""" Constructs an ERNIE-Doc 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 ErnieDocTokenizer tokenizer = ErnieDocTokenizer.from_pretrained('ernie-doc-base-zh') encoded_inputs = tokenizer('He was a puppeteer') """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "ernie-doc-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-zh/vocab.txt", } } pretrained_init_configuration = { "ernie-doc-base-zh": { "do_lower_case": True }, } 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(ErnieDocTokenizer, 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)
[文档]class ErnieDocBPETokenizer(BPETokenizer): r""" Constructs an ERNIE-Doc BPE tokenizer. It uses a bpe tokenizer to do punctuation splitting, lower casing and so on, then tokenize words as subwords. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.BPETokenizer`. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): File path of the vocabulary. encoder_json_path (str, optional): File path of the id to vocab. vocab_bpe_path (str, optional): File path of word merge text. 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 ErnieDocBPETokenizer tokenizer = ErnieDocBPETokenizer.from_pretrained('ernie-doc-base-en') encoded_inputs = tokenizer('He was a puppeteer') """ resource_files_names = { "vocab_file": "vocab.txt", "encoder_json_path": "encoder.json", "vocab_bpe_path": "vocab.bpe" } # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "ernie-doc-base-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-en/vocab.txt" }, "encoder_json_path": { "ernie-doc-base-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-en/encoder.json" }, "vocab_bpe_path": { "ernie-doc-base-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-en/vocab.bpe" } } pretrained_init_configuration = { "ernie-doc-base-en": { "unk_token": "[UNK]" }, } def __init__(self, vocab_file, encoder_json_path="./configs/encoder.json", vocab_bpe_path="./configs/vocab.bpe", unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): super(ErnieDocBPETokenizer, self).__init__( vocab_file, encoder_json_path=encoder_json_path, vocab_bpe_path=vocab_bpe_path, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token) @property def vocab_size(self): """ Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab)