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Our S3, e.g.: dbmdz/bert-base-german-cased.Ī path to a directory containing a configuration file saved using the Pretrained_model_name_or_path ( string) – either:Ī string with the shortcut name of a pre-trained model configuration to load from cache orĪ string with the identifier name of a pre-trained model configuration that was user-uploaded to Instantiate a PretrainedConfig (or a derived class) from a pre-trained model configuration. PretrainedConfig classmethod from_pretrained ( pretrained_model_name_or_path, ** kwargs ) → nfiguration_utils.PretrainedConfig ¶ Json_file ( string) – Path to the JSON file containing the parameters. PretrainedConfig classmethod from_json_file ( json_file : str ) → nfiguration_utils.PretrainedConfig ¶Ĭonstructs a Config from the path to a json file of parameters. Kwargs ( Dict) – Additional parameters from which to initialize the configuration object.Īn instance of a configuration object Return type
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Such a dictionary can be retrievedįrom a pre-trained checkpoint by leveraging the get_config_dict() ParametersĬonfig_dict ( Dict) – Dictionary that will be used to instantiate the configuration object. Torchscript ( bool, optional, defaults to False) – Is the model used with Torchscript (for PyTorch models).Ĭlassmethod from_dict ( config_dict : Dict, ** kwargs ) → nfiguration_utils.PretrainedConfig ¶Ĭonstructs a Config from a Python dictionary of parameters. Output_hidden_states ( string, optional, defaults to False) – Should the model returns all hidden-states.
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Output_attentions ( bool, optional, defaults to False) – Should the model returns attentions weights. Num_labels ( int, optional, defaults to 2) – Number of classes to use when the model is a classification model (sequences/tokens) This can be used when converting from an original (TensorFlow or PyTorch) checkpoint. Model_type: a string that identifies the model type, that we serialize into the JSON file, and that we use to recreate the correct object in AutoConfig.įinetuning_task ( string or None, optional, defaults to None) – Name of the task used to fine-tune the model. Pretrained_config_archive_map: a python dict with shortcut names (string) as keys and url (string) of associated pretrained model configurations as values. Class attributes (overridden by derived classes): It only affects the model’s configuration. Loading the configuration file and using this file to initialize a model does not load the model weights. A configuration file can be loaded and saved to disk.