type=int, default=1) learn_arg.add_argument('--decoder_lr', type=float, default=2e-5) learn_arg.add_argument('--encoder_lr', type=float, default=1e-5) learn_arg.add_argument('--lr_decay', type=float, default=0.01) learn_arg.add_argument('--weight_decay', type=float, default=1e-5) learn_arg.add_argument('--max_grad_norm', type=float, default=0) learn_arg.add_argument('--optimizer', type=str, default='AdamW', choices=['Adam', 'AdamW']) evaluation_arg = add_argument_group('Evaluation') evaluation_arg.add_argument('--n_best_size', type=int, default=100) evaluation_arg.add_argument('--max_span_length', type=int, default=12) #NYT webNLG 10 misc_arg = add_argument_group('MISC') misc_arg.add_argument('--refresh', type=str2bool, default=False) misc_arg.add_argument('--use_gpu', type=str2bool, default=True) misc_arg.add_argument('--visible_gpu', type=int, default=1) misc_arg.add_argument('--random_seed', type=int, default=1) args, unparsed = get_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.visible_gpu) for arg in vars(args): print(arg, ":", getattr(args, arg)) set_seed(args.random_seed) data = build_data(args) model = SetPred4RE(args, data.relational_alphabet.size()) trainer = Trainer(model, data, args) trainer.train_model()