paddlenlp.transformers.ppminilm.tokenizer 源代码

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# 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, WordpieceTokenizer

__all__ = ['PPMiniLMTokenizer']


[文档]class PPMiniLMTokenizer(PretrainedTokenizer): r""" Constructs an PPMiniLM 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.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): 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 PPMiniLMTokenizer tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h') 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": { "ppminilm-6l-768h": "https://bj.bcebos.com/paddlenlp/models/transformers/ppminilm-6l-768h/vocab.txt", } } pretrained_init_configuration = { "ppminilm-6l-768h": { "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): 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 = PPMiniLMTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.do_lower_case = do_lower_case self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token) @property def vocab_size(self): """ Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab) def _tokenize(self, text): r""" End-to-end tokenization for PPMiniM models. Args: text (str): The text to be tokenized. Returns: List[str]: A list of string representing converted tokens. """ split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) return split_tokens
[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 PPMiniLMTokenizer tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h') 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 num_special_tokens_to_add(self, pair=False): r""" 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 """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None))
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): r""" Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` Args: token_ids_0 (List[int]): List of IDs to which the special tokens will be added. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: List[int]: List of input_id with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): r""" Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. An offset_mapping has the following format: - single sequence: ``(0,0) X (0,0)`` - pair of sequences: ``(0,0) A (0,0) B (0,0)`` Args: offset_mapping_ids_0 (List[tuple]): List of char offsets to which the special tokens will be added. offset_mapping_ids_1 (List[tuple], optional): Optional second list of wordpiece offsets for offset mapping pairs. Defaults to `None`. Returns: List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return [(0, 0)] + offset_mapping_0 + [(0, 0)] return [(0, 0)] + offset_mapping_0 + [(0, 0) ] + offset_mapping_1 + [(0, 0)]
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): r""" Create a mask from the two sequences passed to be used in a sequence-pair classification task. A sequence pair 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 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). 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 IDs for sequence pairs. Defaults to `None`. Returns: List[int]: List of token_type_id according to the given sequence(s). """ _sep = [self.sep_token_id] _cls = [self.cls_token_id] if token_ids_1 is None: return len(_cls + token_ids_0 + _sep) * [0] return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1]