Source code for paddlenlp.transformers.squeezebert.tokenizer

# 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");
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import os

from paddlenlp.transformers import (

__all__ = [

[docs]class SqueezeBertTokenizer(PretrainedTokenizer): """ Constructs a SqueezeBert 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): file path of the vocabulary do_lower_case (bool): Whether the text strips accents and convert to lower case. Default: `True`. Default: True. unk_token (str): The special token for unkown words. Default: "[UNK]". sep_token (str): The special token for separator token . Default: "[SEP]". pad_token (str): The special token for padding. Default: "[PAD]". cls_token (str): The special token for cls. Default: "[CLS]". mask_token (str): The special token for mask. Default: "[MASK]". Examples: .. code-block:: python from paddlenlp.transformers import SqueezeBertTokenizer tokenizer = SqueezeBertTokenizer.from_pretrained('squeezebert-uncased') # the following line get: ['he', 'was', 'a', 'puppet', '##eer'] tokens = tokenizer('He was a puppeteer') # the following line get: 'he was a puppeteer' tokenizer.convert_tokens_to_string(tokens) """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "squeezebert-uncased": "", "squeezebert-mnli": "", "squeezebert-mnli-headless": "", } } pretrained_init_configuration = { "squeezebert-uncased": {"do_lower_case": True}, "squeezebert-mnli": {"do_lower_case": True}, "squeezebert-mnli-headless": {"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 = SqueezeBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file) ) 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)
[docs] def get_vocab(self): return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)
def _tokenize(self, text): """ End-to-end tokenization for SqueezeBert models. Args: text (str): The text to be tokenized. Returns: list: 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
[docs] def convert_tokens_to_string(self, tokens): """ 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): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[docs] def num_special_tokens_to_add(self, pair=False): """ 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: Returns the number of added tokens in the case of a sequence pair if set to True, returns the number of added tokens in the case of a single sequence if set to False. Returns: 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))
[docs] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A SqueezeBert sequence has the following format: :: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` Args: token_ids_0 (:obj:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`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
[docs] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. A SqueezeBert 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 (:obj:`List[tuple]`): List of char offsets to which the special tokens will be added. offset_mapping_ids_1 (:obj:`List[tuple]`, `optional`): Optional second list of char offsets for offset mapping pairs. Returns: :obj:`List[tuple]`: List of char 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)]
[docs] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBert 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 :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`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]
[docs] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): List of ids of the first sequence. token_ids_1 (List[int], optional): List of ids of the second sequence. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to None. Returns: results (List[int]): The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formatted with special tokens for the model." ) return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1]