mirror of
https://github.com/huggingface/transformers.git
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498 lines
36 KiB
Python
498 lines
36 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 .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
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from .file_utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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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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that will be instantiated as one of the base model classes of the library
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when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method takes 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`: DistilBertModel (DistilBERT model)
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- contains `roberta`: RobertaModel (RoBERTa model)
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- contains `bert`: BertModel (Bert model)
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- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
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- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
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- contains `xlnet`: XLNetModel (XLNet model)
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- contains `xlm`: XLMModel (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoModel is designed to be instantiated "
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"using the `AutoModel.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, *model_args, **kwargs):
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r""" Instantiates one of the base model classes of the library
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from a pre-trained model configuration.
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The 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`: DistilBertModel (DistilBERT model)
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- contains `roberta`: RobertaModel (RoBERTa model)
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- contains `bert`: BertModel (Bert model)
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- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
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- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
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- contains `xlnet`: XLNetModel (XLNet model)
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- contains `xlm`: XLMModel (XLM model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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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 to load from cache or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
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- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
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model_args: (`optional`) Sequence of positional arguments:
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All remaning positional arguments will be passed to the underlying model's ``__init__`` method
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config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
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Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
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- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
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- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
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state_dict: (`optional`) dict:
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an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
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This option can be used if you want to create a model from a pretrained configuration but load your own weights.
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In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
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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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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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output_loading_info: (`optional`) boolean:
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Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
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kwargs: (`optional`) Remaining dictionary of keyword arguments:
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Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
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- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
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- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
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Examples::
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model = AutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
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model = AutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = AutoModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
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assert model.config.output_attention == True
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# Loading from a TF checkpoint file instead of a PyTorch model (slower)
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config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
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model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'distilbert' in pretrained_model_name_or_path:
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return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'roberta' in pretrained_model_name_or_path:
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return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'bert' in pretrained_model_name_or_path:
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return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'gpt2' in pretrained_model_name_or_path:
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return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'transfo-xl' in pretrained_model_name_or_path:
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return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **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 AutoModelWithLMHead(object):
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r"""
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:class:`~pytorch_transformers.AutoModelWithLMHead` is a generic model class
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that will be instantiated as one of the language modeling model classes of the library
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when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method takes 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 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`: DistilBertForMaskedLM (DistilBERT model)
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- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
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- contains `bert`: BertForMaskedLM (Bert model)
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- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
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- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
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- contains `xlnet`: XLNetLMHeadModel (XLNet model)
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- contains `xlm`: XLMWithLMHeadModel (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
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"using the `AutoModelWithLMHead.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, *model_args, **kwargs):
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r""" Instantiates one of the language modeling model classes of the library
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from a pre-trained model configuration.
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The `from_pretrained()` method takes 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 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`: DistilBertForMaskedLM (DistilBERT model)
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- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
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- contains `bert`: BertForMaskedLM (Bert model)
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- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
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- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
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- contains `xlnet`: XLNetLMHeadModel (XLNet model)
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- contains `xlm`: XLMWithLMHeadModel (XLM model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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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 to load from cache or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
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- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
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model_args: (`optional`) Sequence of positional arguments:
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All remaning positional arguments will be passed to the underlying model's ``__init__`` method
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config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
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Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
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- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
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- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
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state_dict: (`optional`) dict:
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an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
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This option can be used if you want to create a model from a pretrained configuration but load your own weights.
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In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
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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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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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output_loading_info: (`optional`) boolean:
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Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
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kwargs: (`optional`) Remaining dictionary of keyword arguments:
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Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
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- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
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- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
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Examples::
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model = AutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
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model = AutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = AutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
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assert model.config.output_attention == True
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# Loading from a TF checkpoint file instead of a PyTorch model (slower)
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config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
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model = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'distilbert' in pretrained_model_name_or_path:
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return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'roberta' in pretrained_model_name_or_path:
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return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'bert' in pretrained_model_name_or_path:
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return BertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'openai-gpt' in pretrained_model_name_or_path:
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return OpenAIGPTLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'gpt2' in pretrained_model_name_or_path:
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return GPT2LMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'transfo-xl' in pretrained_model_name_or_path:
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return TransfoXLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlnet' in pretrained_model_name_or_path:
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return XLNetLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'xlm' in pretrained_model_name_or_path:
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return XLMWithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **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 AutoModelForSequenceClassification(object):
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r"""
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:class:`~pytorch_transformers.AutoModelForSequenceClassification` is a generic model class
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that will be instantiated as one of the sequence classification model classes of the library
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when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method takes 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 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`: DistilBertForSequenceClassification (DistilBERT model)
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- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
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- contains `bert`: BertForSequenceClassification (Bert model)
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- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
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- contains `xlm`: XLMForSequenceClassification (XLM model)
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This class cannot be instantiated using `__init__()` (throws an error).
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"""
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def __init__(self):
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raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
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"using the `AutoModelWithLMHead.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, *model_args, **kwargs):
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r""" Instantiates one of the sequence classification model classes of the library
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from a pre-trained model configuration.
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The `from_pretrained()` method takes 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 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`: DistilBertForSequenceClassification (DistilBERT model)
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- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
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- contains `bert`: BertForSequenceClassification (Bert model)
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- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
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- contains `xlm`: XLMForSequenceClassification (XLM model)
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The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
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To train the model, you should first set it back in training mode with `model.train()`
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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 to load from cache or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
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- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
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model_args: (`optional`) Sequence of positional arguments:
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All remaning positional arguments will be passed to the underlying model's ``__init__`` method
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config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
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Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
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- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
|
|
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
|
|
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
|
|
|
|
state_dict: (`optional`) dict:
|
|
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
|
|
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
|
|
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
|
|
|
|
cache_dir: (`optional`) string:
|
|
Path to a directory in which a downloaded pre-trained model
|
|
configuration should be cached if the standard cache should not be used.
|
|
|
|
force_download: (`optional`) boolean, default False:
|
|
Force to (re-)download the model weights and configuration files and override the cached versions if they 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.
|
|
|
|
output_loading_info: (`optional`) boolean:
|
|
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
|
|
|
|
kwargs: (`optional`) Remaining dictionary of keyword arguments:
|
|
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
|
|
|
|
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
|
|
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
|
|
|
|
Examples::
|
|
|
|
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
|
|
model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
|
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
|
|
assert model.config.output_attention == True
|
|
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
|
|
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
|
|
model = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
|
|
|
"""
|
|
if 'distilbert' in pretrained_model_name_or_path:
|
|
return DistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
elif 'roberta' in pretrained_model_name_or_path:
|
|
return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
elif 'bert' in pretrained_model_name_or_path:
|
|
return BertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
elif 'xlnet' in pretrained_model_name_or_path:
|
|
return XLNetForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
elif 'xlm' in pretrained_model_name_or_path:
|
|
return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
|
|
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
|
"'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path))
|
|
|
|
|
|
class AutoModelForQuestionAnswering(object):
|
|
r"""
|
|
:class:`~pytorch_transformers.AutoModelForQuestionAnswering` is a generic model class
|
|
that will be instantiated as one of the question answering model classes of the library
|
|
when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
|
|
class method.
|
|
|
|
The `from_pretrained()` method takes care of returning the correct model class instance
|
|
using pattern matching on the `pretrained_model_name_or_path` string.
|
|
|
|
The model class to instantiate is selected as the first pattern matching
|
|
in the `pretrained_model_name_or_path` string (in the following order):
|
|
- contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
|
|
- contains `bert`: BertForQuestionAnswering (Bert model)
|
|
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
|
- contains `xlm`: XLMForQuestionAnswering (XLM model)
|
|
|
|
This class cannot be instantiated using `__init__()` (throws an error).
|
|
"""
|
|
def __init__(self):
|
|
raise EnvironmentError("AutoModelWithLMHead is designed to be instantiated "
|
|
"using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` method.")
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
|
|
r""" Instantiates one of the question answering model classes of the library
|
|
from a pre-trained model configuration.
|
|
|
|
The `from_pretrained()` method takes care of returning the correct model class instance
|
|
using pattern matching on the `pretrained_model_name_or_path` string.
|
|
|
|
The model class to instantiate is selected as the first pattern matching
|
|
in the `pretrained_model_name_or_path` string (in the following order):
|
|
- contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
|
|
- contains `bert`: BertForQuestionAnswering (Bert model)
|
|
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
|
- contains `xlm`: XLMForQuestionAnswering (XLM model)
|
|
|
|
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
|
|
To train the model, you should first set it back in training mode with `model.train()`
|
|
|
|
Params:
|
|
pretrained_model_name_or_path: either:
|
|
|
|
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
|
|
- a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
|
|
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
|
|
|
|
model_args: (`optional`) Sequence of positional arguments:
|
|
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
|
|
|
|
config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
|
|
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
|
|
|
|
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
|
|
- the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
|
|
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
|
|
|
|
state_dict: (`optional`) dict:
|
|
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
|
|
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
|
|
In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
|
|
|
|
cache_dir: (`optional`) string:
|
|
Path to a directory in which a downloaded pre-trained model
|
|
configuration should be cached if the standard cache should not be used.
|
|
|
|
force_download: (`optional`) boolean, default False:
|
|
Force to (re-)download the model weights and configuration files and override the cached versions if they 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.
|
|
|
|
output_loading_info: (`optional`) boolean:
|
|
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
|
|
|
|
kwargs: (`optional`) Remaining dictionary of keyword arguments:
|
|
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
|
|
|
|
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
|
|
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
|
|
|
|
Examples::
|
|
|
|
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
|
|
model = AutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
|
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
|
|
assert model.config.output_attention == True
|
|
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
|
|
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
|
|
model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
|
|
|
"""
|
|
if 'distilbert' in pretrained_model_name_or_path:
|
|
return DistilBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
elif 'bert' in pretrained_model_name_or_path:
|
|
return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
elif 'xlnet' in pretrained_model_name_or_path:
|
|
return XLNetForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
elif 'xlm' in pretrained_model_name_or_path:
|
|
return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
|
|
|
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
|
"'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path))
|