Source code for transformers.models.t5.tokenization_t5_fast

# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for model T5."""


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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
    from .tokenization_t5 import T5Tokenizer
else:
    T5Tokenizer = None


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}


[docs]class T5TokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" T5 tokenizer (backed by HuggingFace's `tokenizers` library). Based on `Unigram <https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models>`__. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` 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 <https://github.com/google/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 <https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__). additional_special_tokens (:obj:`List[str]`, `optional`): Additional special tokens used by the tokenizer. """ 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"] slow_tokenizer_class = T5Tokenizer prefix_tokens: List[int] = [] def __init__( self, vocab_file, tokenizer_file=None, 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 = [f"<extra_id_{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 special tokens extra_tokens = len(set(filter(lambda x: bool("extra_id_" in str(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__( vocab_file, tokenizer_file=tokenizer_file, 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
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a 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) logger.info(f"Copy vocab file to {out_vocab_file}") return (out_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 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 = token_ids_0 + [self.eos_token_id] if token_ids_1 is None: return self.prefix_tokens + token_ids_0 else: token_ids_1 = token_ids_1 + [self.eos_token_id] return self.prefix_tokens + token_ids_0 + token_ids_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. 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]