Source code for transformers.tokenization_prophetnet

# coding=utf-8
# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import collections
import os
from typing import List, Optional, Tuple

from .tokenization_bert import BasicTokenizer, WordpieceTokenizer
from .tokenization_utils import PreTrainedTokenizer
from .utils import logging

logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "prophetnet.tokenizer"}

    "vocab_file": {
        "microsoft/prophetnet-large-uncased": "",

    "microsoft/prophetnet-large-uncased": {"do_lower_case": True},

    "microsoft/prophetnet-large-uncased": 512,

def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    with open(vocab_file, "r", encoding="utf-8") as reader:
        tokens = reader.readlines()
    for index, token in enumerate(tokens):
        token = token.rstrip("\n")
        vocab[token] = index
    return vocab

[docs]class ProphetNetTokenizer(PreTrainedTokenizer): r""" Construct a ProphetNetTokenizer. Based on WordPiece. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (:obj:`str`): File containing the vocabulary. do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to lowercase the input when tokenizing. do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to do basic tokenization before WordPiece. never_split (:obj:`Iterable`, `optional`): Collection of tokens which will never be split during tokenization. Only has an effect when :obj:`do_basic_tokenize=True` unk_token (:obj:`str`, `optional`, defaults to :obj:`"[UNK]"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. x_sep_token (:obj:`str`, `optional`, defaults to :obj:`"[X_SEP]"`): Special second separator token, which can be generated by :class:`~transformers.ProphetNetForConditionalGeneration`. It is used to separate bullet-point like sentences in summarization, *e.g.*. pad_token (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this `issue <>`__). strip_accents: (:obj:`bool`, `optional`): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for :obj:`lowercase` (as in the original BERT). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token="[UNK]", sep_token="[SEP]", x_sep_token="[X_SEP]", pad_token="[PAD]", mask_token="[MASK]", tokenize_chinese_chars=True, strip_accents=None, **kwargs ): super().__init__( do_lower_case=do_lower_case, do_basic_tokenize=do_basic_tokenize, never_split=never_split, unk_token=unk_token, sep_token=sep_token, x_sep_token=x_sep_token, pad_token=pad_token, mask_token=mask_token, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, **kwargs, ) self.unique_no_split_tokens.append(x_sep_token) if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained " "model use `tokenizer = ProphetNetTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file) ) self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) self.do_basic_tokenize = do_basic_tokenize if do_basic_tokenize: self.basic_tokenizer = BasicTokenizer( do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, ) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token) @property def vocab_size(self): return len(self.vocab)
[docs] def get_vocab(self): return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text): split_tokens = [] if self.do_basic_tokenize: for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens): # If the token is part of the never_split set if token in self.basic_tokenizer.never_split: split_tokens.append(token) else: split_tokens += self.wordpiece_tokenizer.tokenize(token) else: split_tokens = self.wordpiece_tokenizer.tokenize(text) return split_tokens def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ return self.vocab.get(token, self.vocab.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.ids_to_tokens.get(index, self.unk_token)
[docs] def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (string) in a single string. """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[docs] def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``prepare_for_model`` method. 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. already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the token list is already formatted with special tokens for the model. Returns: :obj:`List[int]`: A 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 None: return ([0] * len(token_ids_0)) + [1] return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
[docs] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ProphetNet 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 IDs <../glossary.html#token-type-ids>`_ according to the given sequence(s). """ sep = [self.sep_token_id] if token_ids_1 is None: return len(token_ids_0 + sep) * [0] return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: index = 0 if os.path.isdir(save_directory): vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) else: vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory with open(vocab_file, "w", encoding="utf-8") as writer: for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( "Saving vocabulary to {}: vocabulary indices are not consecutive." " Please check that the vocabulary is not corrupted!".format(vocab_file) ) index = token_index writer.write(token + "\n") index += 1 return (vocab_file,)
[docs] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERT 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 IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. """ if token_ids_1 is None: return token_ids_0 + [self.sep_token_id] sep = [self.sep_token_id] return token_ids_0 + sep + token_ids_1 + sep