def get_hp_value_name(config_dict):
    """
    Generate a string which store hyper-parameter values as part of the model name.
    This is useful for hp-optimization, if you decide to perform one.

    :param config_dict: configuration dictionary retrieved from the .yaml file.
    :return: concatenated hp values (string)
    """

    seed = config_dict.get('random_seed', 1)
    embed = config_dict['model_params']['embedding_dim']
    hidden_size = config_dict['model_params']['encoder_params']['hidden_size']
    dropout = config_dict['model_params']['encoder_params']['dropout']
    batch_size = config_dict['training_params']['batch_size']
    lr = config_dict['training_params']['learning_rate']

    return 'seed%s-emb%s-hid%s-drop%s-bs%s-lr%s' % (seed, embed, hidden_size, dropout, batch_size, lr)