Source code for transformers.tokenization_auto

# coding=utf-8
# Copyright 2018 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
#
#     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.
""" Auto Model class. """


import logging
from collections import OrderedDict

from .configuration_auto import (
    AlbertConfig,
    AutoConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    FlaubertConfig,
    GPT2Config,
    OpenAIGPTConfig,
    RobertaConfig,
    T5Config,
    TransfoXLConfig,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
)
from .configuration_utils import PretrainedConfig
from .tokenization_albert import AlbertTokenizer
from .tokenization_bert import BertTokenizer
from .tokenization_bert_japanese import BertJapaneseTokenizer
from .tokenization_camembert import CamembertTokenizer
from .tokenization_ctrl import CTRLTokenizer
from .tokenization_distilbert import DistilBertTokenizer
from .tokenization_flaubert import FlaubertTokenizer
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_t5 import T5Tokenizer
from .tokenization_transfo_xl import TransfoXLTokenizer
from .tokenization_xlm import XLMTokenizer
from .tokenization_xlm_roberta import XLMRobertaTokenizer
from .tokenization_xlnet import XLNetTokenizer


logger = logging.getLogger(__name__)


TOKENIZER_MAPPING = OrderedDict(
    [
        (T5Config, T5Tokenizer),
        (DistilBertConfig, DistilBertTokenizer),
        (AlbertConfig, AlbertTokenizer),
        (CamembertConfig, CamembertTokenizer),
        (XLMRobertaConfig, XLMRobertaTokenizer),
        (RobertaConfig, RobertaTokenizer),
        (BertConfig, BertTokenizer),
        (OpenAIGPTConfig, OpenAIGPTTokenizer),
        (GPT2Config, GPT2Tokenizer),
        (TransfoXLConfig, TransfoXLTokenizer),
        (XLNetConfig, XLNetTokenizer),
        (FlaubertConfig, FlaubertTokenizer),
        (XLMConfig, XLMTokenizer),
        (CTRLConfig, CTRLTokenizer),
    ]
)


[docs]class AutoTokenizer: r""":class:`~transformers.AutoTokenizer` is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` class method. The `from_pretrained()` method take care of returning the correct tokenizer class instance based on the `model_type` property of the config object, or when it's missing, falling back to using pattern matching on the `pretrained_model_name_or_path` string. The tokenizer class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `t5`: T5Tokenizer (T5 model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) - contains `albert`: AlbertTokenizer (ALBERT model) - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model) - contains `roberta`: RobertaTokenizer (RoBERTa model) - contains `bert`: BertTokenizer (Bert model) - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model) - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model) - contains `xlnet`: XLNetTokenizer (XLNet model) - contains `xlm`: XLMTokenizer (XLM model) - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) This class cannot be instantiated using `__init__()` (throw an error). """ def __init__(self): raise EnvironmentError( "AutoTokenizer is designed to be instantiated " "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method." )
[docs] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): r""" Instantiate one of the tokenizer classes of the library from a pre-trained model vocabulary. The tokenizer class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `t5`: T5Tokenizer (T5 model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) - contains `albert`: AlbertTokenizer (ALBERT model) - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model) - contains `roberta`: RobertaTokenizer (RoBERTa model) - contains `bert-base-japanese`: BertJapaneseTokenizer (Bert model) - contains `bert`: BertTokenizer (Bert model) - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model) - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model) - contains `xlnet`: XLNetTokenizer (XLNet model) - contains `xlm`: XLMTokenizer (XLM model) - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) Params: pretrained_model_name_or_path: either: - a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``. - a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``. - (not applicable to all derived classes) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``. cache_dir: (`optional`) string: Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used. force_download: (`optional`) boolean, default False: Force to (re-)download the vocabulary files and override the cached versions if they exists. resume_download: (`optional`) boolean, default False: Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method. kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details. Examples:: # Download vocabulary from S3 and cache. tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # Download vocabulary from S3 (user-uploaded) and cache. tokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-base-german-cased') # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`) tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/') """ config = kwargs.pop("config", None) if not isinstance(config, PretrainedConfig): config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) if "bert-base-japanese" in pretrained_model_name_or_path: return BertJapaneseTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) for config_class, tokenizer_class in TOKENIZER_MAPPING.items(): if isinstance(config, config_class): return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) raise ValueError( "Unrecognized configuration class {} to build an AutoTokenizer.\n" "Model type should be one of {}.".format( config.__class__, ", ".join(c.__name__ for c in TOKENIZER_MAPPING.keys()) ) )