split configuration and modeling files
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thomwolf
@@ -18,125 +18,22 @@ from __future__ import absolute_import, division, print_function, unicode_litera
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import logging
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from .modeling_bert import BertConfig, BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
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from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel, OpenAIGPTLMHeadModel
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from .modeling_gpt2 import GPT2Config, GPT2Model, GPT2LMHeadModel
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from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel
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from .modeling_xlnet import XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
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from .modeling_xlm import XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
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from .modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
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from .modeling_distilbert import DistilBertConfig, DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
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from .modeling_bert import BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
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from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel
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from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel
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from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel
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from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
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from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
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from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
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from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
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from .modeling_utils import PreTrainedModel, SequenceSummary, add_start_docstrings
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from .modeling_utils import PreTrainedModel, SequenceSummary
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from .file_utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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class AutoConfig(object):
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r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class
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that will be instantiated as one of the configuration classes of the library
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when created with the `AutoConfig.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 model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The base model 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 `distilbert`: DistilBertConfig (DistilBERT model)
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- contains `bert`: BertConfig (Bert model)
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- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
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- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
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- contains `xlnet`: XLNetConfig (XLNet model)
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- contains `xlm`: XLMConfig (XLM model)
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- contains `roberta`: RobertaConfig (RoBERTa 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("AutoConfig is designed to be instantiated "
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"using the `AutoConfig.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, **kwargs):
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r""" Instantiate a one of the configuration classes of the library
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from a pre-trained model configuration.
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The configuration 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 `distilbert`: DistilBertConfig (DistilBERT model)
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- contains `bert`: BertConfig (Bert model)
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- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
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- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
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- contains `xlnet`: XLNetConfig (XLNet model)
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- contains `xlm`: XLMConfig (XLM model)
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- contains `roberta`: RobertaConfig (RoBERTa 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 or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
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- a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
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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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kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
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- The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
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- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
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force_download: (`optional`) boolean, default False:
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Force to (re-)download the model weights and configuration files and override the cached versions if they 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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return_unused_kwargs: (`optional`) bool:
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- If False, then this function returns just the final configuration object.
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- If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
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Examples::
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config = AutoConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
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config = AutoConfig.from_pretrained('./test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
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config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
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config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
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assert config.output_attention == True
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config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
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foo=False, return_unused_kwargs=True)
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assert config.output_attention == True
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assert unused_kwargs == {'foo': False}
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"""
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if 'distilbert' in pretrained_model_name_or_path:
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return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'roberta' in pretrained_model_name_or_path:
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return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'bert' in pretrained_model_name_or_path:
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return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'gpt2' in pretrained_model_name_or_path:
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return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'transfo-xl' in pretrained_model_name_or_path:
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return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMConfig.from_pretrained(pretrained_model_name_or_path, **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', 'roberta'".format(pretrained_model_name_or_path))
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class AutoModel(object):
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r"""
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:class:`~pytorch_transformers.AutoModel` is a generic model class
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