update names of torch.hub to simpler names - update docstring
This commit is contained in:
thomwolf
parent
c665e0fcfe
commit
7a1f174a9d
14
hubconf.py
14
hubconf.py
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@ -1,10 +1,10 @@
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dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex']
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dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex', 'sentencepiece', 'sacremoses']
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from hubconfs.automodels_hubconf import (
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autoConfig,
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autoModel,
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autoModelForQuestionAnswering,
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autoModelForSequenceClassification,
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autoModelWithLMHead,
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autoTokenizer,
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config,
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model,
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modelForQuestionAnswering,
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modelForSequenceClassification,
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modelWithLMHead,
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tokenizer,
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)
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@ -1,59 +1,20 @@
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from pytorch_transformers import (
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AutoTokenizer, AutoConfig, AutoModel, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForQuestionAnswering
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)
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from pytorch_transformers.modeling_utils import add_start_docstrings
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@add_start_docstrings(AutoConfig.__doc__)
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def config(*args, **kwargs):
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r"""
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# Using torch.hub !
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import torch
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def autoConfig(*args, **kwargs):
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r""" Instantiates 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 `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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config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased') # Download configuration from S3 and cache.
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config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
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config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/my_configuration.json')
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config = torch.hub.load('huggingface/pytorch-transformers', 'config', '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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config, unused_kwargs = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, 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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@ -62,346 +23,88 @@ def autoConfig(*args, **kwargs):
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return AutoConfig.from_pretrained(*args, **kwargs)
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def autoTokenizer(*args, **kwargs):
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r""" Instantiates one of the tokenizer classes of the library
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from a pre-trained model vocabulary.
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@add_start_docstrings(AutoTokenizer.__doc__)
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def tokenizer(*args, **kwargs):
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r"""
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# Using torch.hub !
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import torch
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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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- contains `roberta`: RobertaTokenizer (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 predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
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- a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~pytorch_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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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:`~pytorch_transformers.PreTrainedTokenizer` for details.
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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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tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-uncased') # Download vocabulary from S3 and cache.
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tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', './test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
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"""
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return AutoTokenizer.from_pretrained(*args, **kwargs)
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def autoModel(*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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@add_start_docstrings(AutoModel.__doc__)
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def model(*args, **kwargs):
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r"""
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# Using torch.hub !
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import torch
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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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model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased') # Download model and configuration from S3 and cache.
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model = torch.hub.load('huggingface/pytorch-transformers', 'model', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = torch.hub.load('huggingface/pytorch-transformers', 'model', '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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model = torch.hub.load('huggingface/pytorch-transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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return AutoModel.from_pretrained(*args, **kwargs)
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@add_start_docstrings(AutoModelWithLMHead.__doc__)
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def modelWithLMHead(*args, **kwargs):
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r"""
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# Using torch.hub !
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import torch
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def autoModelWithLMHead(*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`: DistilBertModelForMaskedLM (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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model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', 'bert-base-uncased') # Download model and configuration from S3 and cache.
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model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
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model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', '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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model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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return AutoModelWithLMHead.from_pretrained(*args, **kwargs)
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def autoModelForSequenceClassification(*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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@add_start_docstrings(AutoModelForSequenceClassification.__doc__)
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def modelForSequenceClassification(*args, **kwargs):
|
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r"""
|
||||
# Using torch.hub !
|
||||
import torch
|
||||
|
||||
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`: DistilBertModelForSequenceClassification (DistilBERT model)
|
||||
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
|
||||
- contains `bert`: BertForSequenceClassification (Bert model)
|
||||
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
|
||||
- contains `xlm`: XLMForSequenceClassification (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 = 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
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-uncased') # Download model and configuration from S3 and cache.
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', '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)
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
||||
|
||||
"""
|
||||
|
||||
return AutoModelForSequenceClassification.from_pretrained(*args, **kwargs)
|
||||
|
||||
|
||||
def autoModelForQuestionAnswering(*args, **kwargs):
|
||||
r""" Instantiates one of the question answering model classes of the library
|
||||
from a pre-trained model configuration.
|
||||
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__)
|
||||
def modelForQuestionAnswering(*args, **kwargs):
|
||||
r"""
|
||||
# Using torch.hub !
|
||||
import torch
|
||||
|
||||
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`: DistilBertModelForQuestionAnswering (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
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-base-uncased') # Download model and configuration from S3 and cache.
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', '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)
|
||||
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
|
||||
|
||||
"""
|
||||
return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)
|
||||
|
|
|
|||
Loading…
Reference in New Issue