Source code for transformers.models.t5.tokenization_t5

# coding=utf-8
# Copyright 2018 T5 Authors and 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
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""" Tokenization class for model T5."""

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging

logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}

    "vocab_file": {
        "t5-small": "",
        "t5-base": "",
        "t5-large": "",
        "t5-3b": "",
        "t5-11b": "",

    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,

[docs]class T5Tokenizer(PreTrainedTokenizer): """ Construct a T5 tokenizer. Based on `SentencePiece <>`__. 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`): `SentencePiece <>`__ file (generally has a `.spm` extension) that contains the vocabulary necessary to instantiate a tokenizer. eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`): The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the :obj:`sep_token`. 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. pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`): The token used for padding, for example when batching sequences of different lengths. extra_ids (:obj:`int`, `optional`, defaults to 100): Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary like in T5 preprocessing see `here <>`__). additional_special_tokens (:obj:`List[str]`, `optional`): Additional special tokens used by the tokenizer. Attributes: sp_model (:obj:`SentencePieceProcessor`): The `SentencePiece` processor that is used for every conversion (string, tokens and IDs). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, eos_token="</s>", unk_token="<unk>", pad_token="<pad>", extra_ids=100, additional_special_tokens=None, **kwargs ): # Add extra_ids to the special token list if extra_ids > 0 and additional_special_tokens is None: additional_special_tokens = ["<extra_id_{}>".format(i) for i in range(extra_ids)] elif extra_ids > 0 and additional_special_tokens is not None: # Check that we have the right number of extra_id special tokens extra_tokens = len(set(filter(lambda x: bool("extra_id" in x), additional_special_tokens))) if extra_tokens != extra_ids: raise ValueError( f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to T5Tokenizer. " "In this case the additional_special_tokens must include the extra_ids tokens" ) super().__init__( eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, extra_ids=extra_ids, additional_special_tokens=additional_special_tokens, **kwargs, ) self.vocab_file = vocab_file self._extra_ids = extra_ids self.sp_model = spm.SentencePieceProcessor() self.sp_model.Load(vocab_file) @property def vocab_size(self): return self.sp_model.get_piece_size() + self._extra_ids def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab
[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)) # normal case: some special tokens 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]
def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]: """Do not add eos again if user already added it.""" if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added." ) return token_ids else: return token_ids + [self.eos_token_id]
[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. T5 does not make use of token type ids, therefore a list of zeros is returned. 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 zeros. """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
[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 sequence has the following format: - single sequence: ``X </s>`` - pair of sequences: ``A </s> B </s>`` 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. """ token_ids_0 = self._add_eos_if_not_present(token_ids_0) if token_ids_1 is None: return token_ids_0 else: token_ids_1 = self._add_eos_if_not_present(token_ids_1) return token_ids_0 + token_ids_1
def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d self.sp_model = spm.SentencePieceProcessor() self.sp_model.Load(self.vocab_file) def _tokenize(self, text, sample=False): """Take as input a string and return a list of strings (tokens) for words/sub-words""" if not sample: pieces = self.sp_model.EncodeAsPieces(text) else: pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1) return pieces def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ if token.startswith("<extra_id_"): match = re.match(r"<extra_id_(\d+)>", token) num = int( return self.vocab_size - num - 1 return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index < self.sp_model.get_piece_size(): token = self.sp_model.IdToPiece(index) else: token = "<extra_id_{}>".format(self.vocab_size - 1 - index) return token def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (string) in a single string. """ current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " " current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode_pieces(current_sub_tokens) return out_string.strip()
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file)"Copy vocab file to {out_vocab_file}") return (out_vocab_file,)