Source code for transformers.modeling_auto

# coding=utf-8
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""" Auto Model class. """

from __future__ import absolute_import, division, print_function, unicode_literals

import logging

from .modeling_bert import BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel
from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel
from .modeling_ctrl import CTRLModel, CTRLLMHeadModel
from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel
from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice

from .modeling_utils import PreTrainedModel, SequenceSummary

from .file_utils import add_start_docstrings

logger = logging.getLogger(__name__)


[docs]class AutoModel(object): r""" :class:`~transformers.AutoModel` is a generic model class that will be instantiated as one of the base model classes of the library when created with the `AutoModel.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`: DistilBertModel (DistilBERT model) - contains `camembert`: CamembertModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: GPT2Model (OpenAI GPT-2 model) - contains `ctrl`: CTRLModel (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLModel (Transformer-XL model) - contains `xlnet`: XLNetModel (XLNet model) - contains `xlm`: XLMModel (XLM model) This class cannot be instantiated using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError("AutoModel is designed to be instantiated " "using the `AutoModel.from_pretrained(pretrained_model_name_or_path)` method.")
[docs] @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`: DistilBertModel (DistilBERT model) - contains `camembert`: CamembertModel (CamemBERT model) - contains `roberta`: RobertaModel (RoBERTa model) - contains `bert`: BertModel (Bert model) - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model) - contains `gpt2`: GPT2Model (OpenAI GPT-2 model) - contains `ctrl`: CTRLModel (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLModel (Transformer-XL model) - contains `xlnet`: XLNetModel (XLNet model) - contains `xlm`: XLMModel (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:`~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:`~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:`~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:`~transformers.PreTrainedModel.save_pretrained` and :func:`~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:`~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 = AutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. model = AutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = AutoModel.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 = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'camembert' in pretrained_model_name_or_path: return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'openai-gpt' in pretrained_model_name_or_path: return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'gpt2' in pretrained_model_name_or_path: return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'transfo-xl' in pretrained_model_name_or_path: return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlnet' in pretrained_model_name_or_path: return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlm' in pretrained_model_name_or_path: return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'ctrl' in pretrained_model_name_or_path: return CTRLModel.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, 'ctrl'".format(pretrained_model_name_or_path))
class AutoModelWithLMHead(object): r""" :class:`~transformers.AutoModelWithLMHead` is a generic model class that will be instantiated as one of the language modeling model classes of the library when created with the `AutoModelWithLMHead.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`: DistilBertForMaskedLM (DistilBERT model) - contains `camembert`: CamembertForMaskedLM (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model) - contains `ctrl`: CTRLLMModel (Salesforce CTRL model) - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model) - contains `xlnet`: XLNetLMHeadModel (XLNet model) - contains `xlm`: XLMWithLMHeadModel (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 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`: DistilBertForMaskedLM (DistilBERT model) - contains `camembert`: CamembertForMaskedLM (CamemBERT model) - contains `roberta`: RobertaForMaskedLM (RoBERTa model) - contains `bert`: BertForMaskedLM (Bert model) - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model) - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model) - contains `xlnet`: XLNetLMHeadModel (XLNet model) - contains `xlm`: XLMWithLMHeadModel (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:`~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:`~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:`~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:`~transformers.PreTrainedModel.save_pretrained` and :func:`~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:`~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 = AutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache. model = AutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = AutoModelWithLMHead.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 = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ if 'distilbert' in pretrained_model_name_or_path: return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'camembert' in pretrained_model_name_or_path: return CamembertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'roberta' in pretrained_model_name_or_path: return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'bert' in pretrained_model_name_or_path: return BertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'openai-gpt' in pretrained_model_name_or_path: return OpenAIGPTLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'gpt2' in pretrained_model_name_or_path: return GPT2LMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'transfo-xl' in pretrained_model_name_or_path: return TransfoXLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlnet' in pretrained_model_name_or_path: return XLNetLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'xlm' in pretrained_model_name_or_path: return XLMWithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif 'ctrl' in pretrained_model_name_or_path: return CTRLLMHeadModel.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','ctrl'".format(pretrained_model_name_or_path)) class AutoModelForSequenceClassification(object): r""" :class:`~transformers.AutoModelForSequenceClassification` is a generic model class that will be instantiated as one of the sequence classification model classes of the library when created with the `AutoModelForSequenceClassification.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`: DistilBertForSequenceClassification (DistilBERT model) - contains `camembert`: CamembertForSequenceClassification (CamemBERT model) - contains `roberta`: RobertaForSequenceClassification (RoBERTa model) - contains `bert`: BertForSequenceClassification (Bert model) - contains `xlnet`: XLNetForSequenceClassification (XLNet model) - contains `xlm`: XLMForSequenceClassification (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 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`: DistilBertForSequenceClassification (DistilBERT model) - contains `camembert`: CamembertForSequenceClassification (CamemBERT 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:`~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:`~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:`~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:`~transformers.PreTrainedModel.save_pretrained` and :func:`~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:`~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 'camembert' in pretrained_model_name_or_path: return CamembertForSequenceClassification.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:`~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:`~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:`~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:`~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:`~transformers.PreTrainedModel.save_pretrained` and :func:`~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:`~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))