190 lines
10 KiB
Python
190 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2018 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Auto Model class. """
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import logging
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from .configuration_auto import (
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AlbertConfig,
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AutoConfig,
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BertConfig,
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CamembertConfig,
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CTRLConfig,
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DistilBertConfig,
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GPT2Config,
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OpenAIGPTConfig,
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RobertaConfig,
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T5Config,
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TransfoXLConfig,
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XLMConfig,
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XLMRobertaConfig,
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XLNetConfig,
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)
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from .configuration_utils import PretrainedConfig
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from .tokenization_albert import AlbertTokenizer
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from .tokenization_bert import BertTokenizer
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from .tokenization_bert_japanese import BertJapaneseTokenizer
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from .tokenization_camembert import CamembertTokenizer
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from .tokenization_ctrl import CTRLTokenizer
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from .tokenization_distilbert import DistilBertTokenizer
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from .tokenization_gpt2 import GPT2Tokenizer
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from .tokenization_openai import OpenAIGPTTokenizer
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from .tokenization_roberta import RobertaTokenizer
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from .tokenization_t5 import T5Tokenizer
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from .tokenization_transfo_xl import TransfoXLTokenizer
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from .tokenization_xlm import XLMTokenizer
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from .tokenization_xlm_roberta import XLMRobertaTokenizer
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from .tokenization_xlnet import XLNetTokenizer
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logger = logging.getLogger(__name__)
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class AutoTokenizer(object):
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r""":class:`~transformers.AutoTokenizer` is a generic tokenizer class
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that will be instantiated as one of the tokenizer classes of the library
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when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method take care of returning the correct tokenizer class instance
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based on the `model_type` property of the config object, or when it's missing,
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falling back to using pattern matching on the `pretrained_model_name_or_path` string.
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The tokenizer class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `t5`: T5Tokenizer (T5 model)
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- contains `distilbert`: DistilBertTokenizer (DistilBert model)
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- contains `albert`: AlbertTokenizer (ALBERT model)
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- contains `camembert`: CamembertTokenizer (CamemBERT model)
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- contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model)
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- contains `roberta`: RobertaTokenizer (RoBERTa model)
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- contains `bert`: BertTokenizer (Bert model)
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- contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
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- contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
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- contains `xlnet`: XLNetTokenizer (XLNet model)
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- contains `xlm`: XLMTokenizer (XLM model)
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- contains `ctrl`: CTRLTokenizer (Salesforce CTRL model)
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This class cannot be instantiated using `__init__()` (throw an error).
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"""
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def __init__(self):
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raise EnvironmentError(
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"AutoTokenizer is designed to be instantiated "
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"using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method."
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)
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
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r""" Instantiate one of the tokenizer classes of the library
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from a pre-trained model vocabulary.
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The tokenizer class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `t5`: T5Tokenizer (T5 model)
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- contains `distilbert`: DistilBertTokenizer (DistilBert model)
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- contains `albert`: AlbertTokenizer (ALBERT model)
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- contains `camembert`: CamembertTokenizer (CamemBERT model)
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- contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model)
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- contains `roberta`: RobertaTokenizer (RoBERTa model)
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- contains `bert-base-japanese`: BertJapaneseTokenizer (Bert model)
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- contains `bert`: BertTokenizer (Bert model)
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- contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
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- contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
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- contains `xlnet`: XLNetTokenizer (XLNet model)
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- contains `xlm`: XLMTokenizer (XLM model)
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- contains `ctrl`: CTRLTokenizer (Salesforce CTRL model)
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Params:
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pretrained_model_name_or_path: either:
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- a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
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- a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
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- 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/``.
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- (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``.
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cache_dir: (`optional`) string:
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Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
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force_download: (`optional`) boolean, default False:
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Force to (re-)download the vocabulary files and override the cached versions if they exists.
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resume_download: (`optional`) boolean, default False:
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Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
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proxies: (`optional`) dict, default None:
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A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
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The proxies are used on each request.
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inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
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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.
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Examples::
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# Download vocabulary from S3 and cache.
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tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
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# Download vocabulary from S3 (user-uploaded) and cache.
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tokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-base-german-cased')
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# If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
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tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/')
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"""
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config = kwargs.pop("config", None)
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if not isinstance(config, PretrainedConfig):
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config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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if "bert-base-japanese" in pretrained_model_name_or_path:
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return BertJapaneseTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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if isinstance(config, T5Config):
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return T5Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, DistilBertConfig):
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return DistilBertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, AlbertConfig):
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return AlbertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, CamembertConfig):
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return CamembertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, XLMRobertaConfig):
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return XLMRobertaTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, RobertaConfig):
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return RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, BertConfig):
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return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, OpenAIGPTConfig):
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return OpenAIGPTTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, GPT2Config):
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return GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, TransfoXLConfig):
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return TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, XLNetConfig):
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return XLNetTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, XLMConfig):
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return XLMTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif isinstance(config, CTRLConfig):
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return CTRLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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raise ValueError(
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"Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm-roberta', 'xlm', 'roberta', 'distilbert,' 'camembert', 'ctrl', 'albert'".format(
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pretrained_model_name_or_path
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)
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)
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