# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Model class. """ from __future__ import absolute_import, division, print_function, unicode_literals import logging from .modeling_tf_bert import TFBertModel, TFBertForMaskedLM, TFBertForSequenceClassification, TFBertForQuestionAnswering from .modeling_tf_openai import TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel from .modeling_tf_gpt2 import TFGPT2Model, TFGPT2LMHeadModel from .modeling_tf_transfo_xl import TFTransfoXLModel, TFTransfoXLLMHeadModel from .modeling_tf_xlnet import TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, TFXLNetForQuestionAnsweringSimple from .modeling_tf_xlm import TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceClassification, TFXLMForQuestionAnsweringSimple from .modeling_tf_roberta import TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification from .modeling_tf_distilbert import TFDistilBertModel, TFDistilBertForQuestionAnswering, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification from .file_utils import add_start_docstrings logger = logging.getLogger(__name__) class TFAutoModel(object): r""" :class:`~pytorch_transformers.TFAutoModel` is a generic model class that will be instantiated as one of the base model classes of the library when created with the `TFAutoModel.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 base 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`: TFDistilBertModel (DistilBERT model) - contains `roberta`: TFRobertaModel (RoBERTa model) - contains `bert`: TFBertModel (Bert model) - contains `openai-gpt`: TFOpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: TFGPT2Model (OpenAI GPT-2 model) - contains `transfo-xl`: TFTransfoXLModel (Transformer-XL model) - contains `xlnet`: TFXLNetModel (XLNet model) - contains `xlm`: TFXLMModel (XLM model) This class cannot be instantiated using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError("TFAutoModel is designed to be instantiated " "using the `TFAutoModel.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 base model classes of the library from a pre-trained model configuration. 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`: TFDistilBertModel (DistilBERT model) - contains `roberta`: TFRobertaModel (RoBERTa model) - contains `bert`: TFTFBertModel (Bert model) - contains `openai-gpt`: TFOpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: TFGPT2Model (OpenAI GPT-2 model) - contains `transfo-xl`: TFTransfoXLModel (Transformer-XL model) - contains `xlnet`: TFXLNetModel (XLNet model) - contains `xlm`: TFXLMModel (XLM model) 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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. from_pt: (`Optional`) Boolean Set to True if the Checkpoint is a PyTorch checkpoint. 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 = TFAutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. model = TFAutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = TFAutoModel.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 = TFAutoModel.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config) """ if 'distilbert' in pretrained_model_name_or_path: return TFDistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return TFRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: return TFBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'openai-gpt' in pretrained_model_name_or_path: return TFOpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'gpt2' in pretrained_model_name_or_path: return TFGPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'transfo-xl' in pretrained_model_name_or_path: return TFTransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlnet' in pretrained_model_name_or_path: return TFXLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlm' in pretrained_model_name_or_path: return TFXLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " "'xlm', 'roberta'".format(pretrained_model_name_or_path)) class TFAutoModelWithLMHead(object): r""" :class:`~pytorch_transformers.TFAutoModelWithLMHead` is a generic model class that will be instantiated as one of the language modeling model classes of the library when created with the `TFAutoModelWithLMHead.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`: TFDistilBertForMaskedLM (DistilBERT model) - contains `roberta`: TFRobertaForMaskedLM (RoBERTa model) - contains `bert`: TFBertForMaskedLM (Bert model) - contains `openai-gpt`: TFOpenAIGPTLMHeadModel (OpenAI GPT model) - contains `gpt2`: TFGPT2LMHeadModel (OpenAI GPT-2 model) - contains `transfo-xl`: TFTransfoXLLMHeadModel (Transformer-XL model) - contains `xlnet`: TFXLNetLMHeadModel (XLNet model) - contains `xlm`: TFXLMWithLMHeadModel (XLM model) This class cannot be instantiated using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError("TFAutoModelWithLMHead is designed to be instantiated " "using the `TFAutoModelWithLMHead.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 language modeling 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`: TFDistilBertForMaskedLM (DistilBERT model) - contains `roberta`: TFRobertaForMaskedLM (RoBERTa model) - contains `bert`: TFBertForMaskedLM (Bert model) - contains `openai-gpt`: TFOpenAIGPTLMHeadModel (OpenAI GPT model) - contains `gpt2`: TFGPT2LMHeadModel (OpenAI GPT-2 model) - contains `transfo-xl`: TFTransfoXLLMHeadModel (Transformer-XL model) - contains `xlnet`: TFXLNetLMHeadModel (XLNet model) - contains `xlm`: TFXLMWithLMHeadModel (XLM model) 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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. from_pt: (`Optional`) Boolean Set to True if the Checkpoint is a PyTorch checkpoint. 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 = TFAutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. model = TFAutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = TFAutoModelWithLMHead.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 = TFAutoModelWithLMHead.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config) """ if 'distilbert' in pretrained_model_name_or_path: return TFDistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return TFRobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: return TFBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'openai-gpt' in pretrained_model_name_or_path: return TFOpenAIGPTLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'gpt2' in pretrained_model_name_or_path: return TFGPT2LMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'transfo-xl' in pretrained_model_name_or_path: return TFTransfoXLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlnet' in pretrained_model_name_or_path: return TFXLNetLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlm' in pretrained_model_name_or_path: return TFXLMWithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) raise ValueError("Unrecognized model identifier in {}. Should contains one of " "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', " "'xlm', 'roberta'".format(pretrained_model_name_or_path)) class TFAutoModelForSequenceClassification(object): r""" :class:`~pytorch_transformers.TFAutoModelForSequenceClassification` is a generic model class that will be instantiated as one of the sequence classification model classes of the library when created with the `TFAutoModelForSequenceClassification.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`: TFDistilBertForSequenceClassification (DistilBERT model) - contains `roberta`: TFRobertaForSequenceClassification (RoBERTa model) - contains `bert`: TFBertForSequenceClassification (Bert model) - contains `xlnet`: TFXLNetForSequenceClassification (XLNet model) - contains `xlm`: TFXLMForSequenceClassification (XLM model) This class cannot be instantiated using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError("TFAutoModelWithLMHead is designed to be instantiated " "using the `TFAutoModelWithLMHead.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 sequence classification 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`: TFDistilBertForSequenceClassification (DistilBERT model) - contains `roberta`: TFRobertaForSequenceClassification (RoBERTa model) - contains `bert`: TFBertForSequenceClassification (Bert model) - contains `xlnet`: TFXLNetForSequenceClassification (XLNet model) - contains `xlm`: TFXLMForSequenceClassification (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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. from_pt: (`Optional`) Boolean Set to True if the Checkpoint is a PyTorch checkpoint. 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 = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. model = TFAutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = TFAutoModelForSequenceClassification.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 = TFAutoModelForSequenceClassification.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config) """ if 'distilbert' in pretrained_model_name_or_path: return TFDistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return TFRobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: return TFBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlnet' in pretrained_model_name_or_path: return TFXLNetForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlm' in pretrained_model_name_or_path: return TFXLMForSequenceClassification.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 TFAutoModelForQuestionAnswering(object): r""" :class:`~pytorch_transformers.TFAutoModelForQuestionAnswering` is a generic model class that will be instantiated as one of the question answering model classes of the library when created with the `TFAutoModelForQuestionAnswering.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`: TFDistilBertForQuestionAnswering (DistilBERT model) - contains `bert`: TFBertForQuestionAnswering (Bert model) - contains `xlnet`: TFXLNetForQuestionAnswering (XLNet model) - contains `xlm`: TFXLMForQuestionAnswering (XLM model) This class cannot be instantiated using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError("TFAutoModelWithLMHead is designed to be instantiated " "using the `TFAutoModelWithLMHead.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`: TFDistilBertForQuestionAnswering (DistilBERT model) - contains `bert`: TFBertForQuestionAnswering (Bert model) - contains `xlnet`: TFXLNetForQuestionAnswering (XLNet model) - contains `xlm`: TFXLMForQuestionAnswering (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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. from_pt: (`Optional`) Boolean Set to True if the Checkpoint is a PyTorch checkpoint. 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 = TFAutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. model = TFAutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = TFAutoModelForQuestionAnswering.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 = TFAutoModelForQuestionAnswering.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config) """ if 'distilbert' in pretrained_model_name_or_path: return TFDistilBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: return TFBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlnet' in pretrained_model_name_or_path: return TFXLNetForQuestionAnsweringSimple.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlm' in pretrained_model_name_or_path: return TFXLMForQuestionAnsweringSimple.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))