paddlenlp.data.tokenizer 源代码

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# Licensed under the Apache License, Version 2.0 (the "License");
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import jieba
from .vocab import Vocab


def get_idx_from_word(word, word_to_idx, unk_word):
    if word in word_to_idx:
        return word_to_idx[word]
    return word_to_idx[unk_word]


class BaseTokenizer(object):
    def __init__(self, vocab):
        self.vocab = vocab

    def get_tokenizer(self):
        return self.tokenizer

    def cut(self, sentence):
        pass

    def encode(self, sentence):
        pass


[文档]class JiebaTokenizer(BaseTokenizer): """ Constructs a tokenizer based on `jieba <https://github.com/fxsjy/jieba>`__. It supports :meth:`cut` method to split the text to tokens, and :meth:`encode` method to covert text to token ids. Args: vocab(paddlenlp.data.Vocab): An instance of :class:`paddlenlp.data.Vocab`. """ def __init__(self, vocab): super(JiebaTokenizer, self).__init__(vocab) self.tokenizer = jieba.Tokenizer() # initialize tokenizer self.tokenizer.FREQ = {key: 1 for key in self.vocab.token_to_idx.keys()} self.tokenizer.total = len(self.tokenizer.FREQ) self.tokenizer.initialized = True
[文档] def cut(self, sentence, cut_all=False, use_hmm=True): """ The method used to cut the text to tokens. Args: sentence(str): The text that needs to be cuted. cut_all(bool, optional): Whether to use the full mode. If True, using full mode that gets all the possible words from the sentence, which is fast but not accurate. If False, using accurate mode that attempts to cut the sentence into the most accurate segmentations, which is suitable for text analysis. Default: False. use_hmm(bool, optional): Whether to use the HMM model. Default: True. Returns: list[str]: A list of tokens. Example: .. code-block:: python from paddlenlp.data import Vocab, JiebaTokenizer # The vocab file. The sample file can be downloaded firstly. # wget https://paddlenlp.bj.bcebos.com/data/senta_word_dict.txt vocab_file_path = './senta_word_dict.txt' # Initialize the Vocab vocab = Vocab.load_vocabulary( vocab_file_path, unk_token='[UNK]', pad_token='[PAD]') tokenizer = JiebaTokenizer(vocab) tokens = tokenizer.cut('我爱你中国') print(tokens) # ['我爱你', '中国'] """ return self.tokenizer.lcut(sentence, cut_all, use_hmm)
[文档] def encode(self, sentence, cut_all=False, use_hmm=True): """ The method used to convert the text to ids. It will firstly call :meth:`cut` method to cut the text to tokens. Then, convert tokens to ids using `vocab`. Args: sentence(str): The text that needs to be cuted. cut_all(bool, optional): Whether to use the full mode. If True, using full mode that gets all the possible words from the sentence, which is fast but not accurate. If False, using accurate mode that attempts to cut the sentence into the most accurate segmentations, which is suitable for text analysis. Default: False. use_hmm(bool, optional): Whether to use the HMM model. Default: True. Returns: list[int]: A list of ids. Example: .. code-block:: python from paddlenlp.data import Vocab, JiebaTokenizer # The vocab file. The sample file can be downloaded firstly. # wget https://paddlenlp.bj.bcebos.com/data/senta_word_dict.txt vocab_file_path = './senta_word_dict.txt' # Initialize the Vocab vocab = Vocab.load_vocabulary( vocab_file_path, unk_token='[UNK]', pad_token='[PAD]') tokenizer = JiebaTokenizer(vocab) ids = tokenizer.encode('我爱你中国') print(ids) # [1170578, 575565] """ words = self.cut(sentence, cut_all, use_hmm) return [ get_idx_from_word(word, self.vocab.token_to_idx, self.vocab.unk_token) for word in words ]