no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': 0.0},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=2e-5, eps=1e-8)
crit = torch.nn.CrossEntropyLoss()
trainer = Train(model_name=model_name,
train_loader=train_loader,
val_loader=val_loader,
test_loader=test_loader,
model=model,
optimizer=optimizer,
loss_fn=crit,
epochs=12,
print_step=1,
early_stop_patience=3,
# save_model_path=f"./save_model/{params['model_name']}",
save_model_path=f"/sdd/yujunshuai/save_model/{model_name}",
save_model_every_epoch=False,
metric=accuracy_score,
num_class=2,
# tensorboard_path='./tensorboard_log')
tensorboard_path='/sdd/yujunshuai/tensorboard_log')
print(trainer.train())
print(trainer.test())
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.lr, eps=1e-8)
trainer = Train(
model_name=args.model_name,
train_loader=train_loader,
test_loader=test_loader,
device=args.device,
model=model,
optimizer=optimizer,
epochs=args.epochs,
print_step=1,
early_stop_patience=args.early_stop_patience,
save_model_path=f"./experiments/save_model/{args.model_name}",
save_model_every_epoch=False,
metric=accuracy_score,
num_class=2,
tensorboard_path=f'./experiments/tensorboard_log/{args.model_name}')
if args.test and args.best_model_path:
print(
trainer.test(test_loader,
'./data/processed_modified_test_data.json',
'./data/processed_modified_test_data_result.json',
args.best_model_path))
else:
print(trainer.train())
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': 0.0},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=2e-5, eps=1e-8)
crit = torch.nn.CrossEntropyLoss()
trainer = Train(model_name='weibo_bert_cat',
train_loader=train_loader,
val_loader=val_loader,
test_loader=test_loader,
model=model,
optimizer=optimizer,
loss_fn=crit,
epochs=10,
print_step=1,
early_stop_patience=3,
# save_model_path=f"./save_model/{params['model_name']}",
save_model_path=f"/sdd/yujunshuai/save_model/weibo_bert_cat",
save_model_every_epoch=False,
metric=accuracy_score,
num_class=2,
# tensorboard_path='./tensorboard_log')
tensorboard_path='/sdd/yujunshuai/tensorboard_log')
trainer.train()
trainer.test()