{
'dev_matched': data_info.datasets['dev_matched'],
'dev_mismatched': data_info.datasets['dev_mismatched']
},
verbose=1))
trainer = Trainer(train_data=data_info.datasets['train'],
model=model,
optimizer=optimizer,
num_workers=0,
batch_size=arg.batch_size,
n_epochs=arg.n_epochs,
print_every=-1,
dev_data=data_info.datasets[arg.devset_name],
metrics=AccuracyMetric(pred="pred", target="target"),
metric_key='acc',
device=[i for i in range(torch.cuda.device_count())],
check_code_level=-1,
callbacks=callbacks,
loss=CrossEntropyLoss(pred="pred", target="target"))
trainer.train(load_best_model=True)
tester = Tester(
data=data_info.datasets[arg.testset_name],
model=model,
metrics=AccuracyMetric(),
batch_size=arg.batch_size,
device=[i for i in range(torch.cuda.device_count())],
)
tester.test()
elif args.optim == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum)
trainer = Trainer(datasets['train'],
model,
optimizer,
loss,
args.batch,
n_epochs=args.epoch,
dev_data=datasets['train'],
metrics=metrics,
device=device,
dev_batch_size=args.test_batch)
trainer.train()
exit(1208)
bigram_embedding_param = list(model.bigram_embed.parameters())
gaz_embedding_param = list(model.lattice_embed.parameters())
embedding_param = bigram_embedding_param
if args.lattice:
gaz_embedding_param = list(model.lattice_embed.parameters())
embedding_param = embedding_param + gaz_embedding_param
embedding_param_ids = list(map(id, embedding_param))
non_embedding_param = list(
filter(lambda x: id(x) not in embedding_param_ids, model.parameters()))
param_ = [{
'params': non_embedding_param
}, {
if not args.debug:
exit(1208)
datasets['train'].apply
if args.see_convergence:
print_info('see_convergence = True')
print_info('so just test train acc|f1')
datasets['train'] = datasets['train'][:100]
if args.optim == 'adam':
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
elif args.optim == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
trainer = Trainer(datasets['train'], model, optimizer, loss, args.batch,
n_epochs=args.epoch, dev_data=datasets['train'], metrics=metrics,
device=device, dev_batch_size=args.test_batch)
trainer.train()
exit(1208)
# if args.warmup and args.model == 'transformer':
# ## warm up start
# if args.optim == 'adam':
# warmup_optimizer = optim.AdamW(model.parameters(),lr=args.warmup_lr,weight_decay=args.weight_decay)
# elif args.optim == 'sgd':
# warmup_optimizer = optim.SGD(model.parameters(),lr=args.warmup_lr,momentum=args.momentum)
#
# warmup_lr_schedule = LRScheduler(lr_scheduler=LambdaLR(warmup_optimizer, lambda ep: 1 * (1 + 0.05)**ep))
# warmup_callbacks = [
# warmup_lr_schedule,
# ]
#
# warmup_trainer = Trainer(datasets['train'],model,warmup_optimizer,loss,args.warmup_batch,
