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101 lines
5.2 KiB
Python
101 lines
5.2 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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from __future__ import absolute_import, division, print_function, unicode_literals
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import logging
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from .tokenization_bert import BertTokenizer
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from .tokenization_openai import OpenAIGPTTokenizer
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from .tokenization_gpt2 import GPT2Tokenizer
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from .tokenization_transfo_xl import TransfoXLTokenizer
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from .tokenization_xlnet import XLNetTokenizer
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from .tokenization_xlm import XLMTokenizer
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logger = logging.getLogger(__name__)
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class AutoTokenizer(object):
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r""":class:`~pytorch_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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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 `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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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("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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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
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r""" Instantiate a 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 `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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Params:
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**pretrained_model_name_or_path**: either:
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- a string with the `shortcut name` of a pre-trained model configuration to load from cache
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or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
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- a path to a `directory` containing a configuration file saved
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using the `save_pretrained(save_directory)` method.
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- a path or url to a saved configuration `file`.
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**cache_dir**: (`optional`) string:
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Path to a directory in which a downloaded pre-trained model
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configuration should be cached if the standard cache should not be used.
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Examples::
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config = AutoTokenizer.from_pretrained('bert-base-uncased') # Download vocabulary from S3 and cache.
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config = AutoTokenizer.from_pretrained('./test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
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"""
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if 'bert' in pretrained_model_name_or_path:
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return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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return OpenAIGPTTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif 'gpt2' in pretrained_model_name_or_path:
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return GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif 'transfo-xl' in pretrained_model_name_or_path:
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return TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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raise ValueError("Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm'".format(pretrained_model_name_or_path))
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