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()
