def get_model_specific_argparser()

in mico/utils/utils.py [0:0]

• 75 lines of code
• 1 McCabe index (conditional complexity)

def get_model_specific_argparser():
    parser = argparse.ArgumentParser()

    parser.add_argument('--model_path', type=str)
    parser.add_argument('--train_folder_path', type=str)
    parser.add_argument('--test_folder_path', type=str)
    parser.add_argument('--number_clusters', type=int, default=16,
                        help='number of clusters to which we are assigning documents and routing queries [%(default)d]')
    parser.add_argument('--dim_hidden', type=int, default=24,
                        help='dimension of hidden state [%(default)d]')
    parser.add_argument('--dim_input', type=int, default=24,
                        help='dimension of input [%(default)d]')
    parser.add_argument('--batch_size', type=int, default=16,
                        help='batch size [%(default)d]')
    parser.add_argument('--batch_size_test', type=int, default=16,
                        help='batch size for evaluating the test dataset [%(default)d]')
    parser.add_argument('--val_ratio', type=float, default=0.1,
                        help='proportion of training data used as validation [%(default)g]')
    parser.add_argument('--lr', type=float, default=0.01,
                        help='initial learning rate [%(default)g]')
    parser.add_argument('--init', type=float, default=0.1,
                        help='unif init range (default if 0) [%(default)g]')
    parser.add_argument('--clip', type=float, default=10,
                        help='gradient clipping [%(default)g]')
    parser.add_argument('--epochs', type=int, default=40,
                        help='max number of epochs [%(default)d]')
    parser.add_argument('--save_per_num_epoch', type=int, default=1,
                        help='save model per number of epochs [%(default)d]')
    parser.add_argument('--log_interval', type=int, default=100,
                        help='number of updates for check [%(default)d]')
    parser.add_argument('--check_val_test_interval', type=int, default=10000,
                        help='number of updates for check [%(default)d]')
    parser.add_argument('--num_bad_epochs', type=int, default=10000,
                        help='num indulged bad epochs [%(default)d]')
    parser.add_argument('--early_quit', type=int, default=0,
                        help='num batches training before end the epoch [%(default)d]')
    parser.add_argument('--num_workers', type=int, default=0,
                        help='num dataloader workers [%(default)d]')
    parser.add_argument('--seed', type=int, default=9061,
                        help='random seed [%(default)d]')
    parser.add_argument('--resume', action='store_true',
                        help='Resume the training process.')
    parser.add_argument('--not_resume_hparams', action='store_true',
                        help='Resume the training process and use newly assigned hyper-parameters.')
    parser.add_argument('--cuda', action='store_true',
                        help='use CUDA?')
    parser.add_argument('--eval_only', action='store_true',
                        help='We only evaluate the trained model.')
    parser.add_argument('--bert_fix', action='store_true',
                        help='Finetune BERT or not.')
    parser.add_argument('--is_csv_header', action='store_true',
                        help='If the input CSV files has header (so we skip the first line).')

    parser.add_argument('--num_layers_posterior', type=int, default=0,
                        help='num layers in posterior [%(default)d]')
    parser.add_argument('--num_steps_prior', type=int, default=4,
                        help='num gradient steps on prior per loss '
                                '[%(default)d]')
    parser.add_argument('--lr_prior', type=float, default=0.1,
                        help='initial learning rate for prior (same as lr '
                                ' if -1) [%(default)g]')
    parser.add_argument('--entropy_weight', type=float, default=2,
                        help='entropy weight in MI [%(default)g]')

    # BERT related
    parser.add_argument('--lr_bert', type=float, default=-1,
                        help='initial learning rate for BERT (same as lr '
                                ' if -1) [%(default)g]')
    parser.add_argument('--max_length', type=int, default=4,
                        help='max number of tokens in a sentence for BERT input '
                                '[%(default)d]')
    parser.add_argument('--num_warmup_steps', type=int, default=0,
                        help='warmup steps (linearly increase learning rate)'
                                '[%(default)d]')
    parser.add_argument('--pooling_strategy', type=str, default='REDUCE_MEAN',
                        help='REDUCE_MEAN or CLS_TOKEN [%(default)s]')
    parser.add_argument('--selected_layer_idx', type=int, default=-1,
                        help='output from which layer [%(default)d]')

    return parser