Method get_config_dict

bert/configuration_utils.py:204–266

From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a Config using `from_dict`. Parameters: pretrained_model_name_or_path (:obj:`string`): The identifier of the pre-trained checkpoint from w

(cls, pretrained_model_name_or_path: str, **kwargs)

    @classmethod
    def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict, Dict]:
        """
        From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used
        for instantiating a Config using `from_dict`.

        Parameters:
            pretrained_model_name_or_path (:obj:`string`):
                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.

        Returns:
            :obj:`Tuple[Dict, Dict]`: The dictionary that will be used to instantiate the configuration object.

        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)

        if os.path.isdir(pretrained_model_name_or_path):
            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
        elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
            config_file = pretrained_model_name_or_path
        else:
            config_file = hf_bucket_url(pretrained_model_name_or_path, filename=CONFIG_NAME, use_cdn=False)

        try:
            # Load from URL or cache if already cached
            resolved_config_file = cached_path(
                config_file,
                cache_dir=cache_dir,
                force_download=force_download,
                proxies=proxies,
                resume_download=resume_download,
                local_files_only=local_files_only,
            )
            # Load config dict
            if resolved_config_file is None:
                raise EnvironmentError
            config_dict = cls._dict_from_json_file(resolved_config_file)

        except EnvironmentError:
            msg = (
                f"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
                f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
                f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
            )
            raise EnvironmentError(msg)

        except json.JSONDecodeError:
            msg = (
                "Couldn't reach server at '{}' to download configuration file or "
                "configuration file is not a valid JSON file. "
                "Please check network or file content here: {}.".format(config_file, resolved_config_file)
            )
            raise EnvironmentError(msg)

        if resolved_config_file == config_file: