def test_itf():
tokenizer = Tokenizer.from_pretrained(Config.model_name)
if Config.use_pickle:
with open(f'{Config.pickle_path}', 'rb') as f:
train_data = pickle.load(f)
else:
train_data = make_train_data_from_txt(Config, tokenizer)
counter, itf = make_itf(train_data, Config.vocab_size, tokenizer)
# itf = (itf - itf.min()) / (itf.max() - itf.min())
# for i in range(itf.size(0)):
# print(i, itf[i])
# itf[itf == 0] += 1e-6
for k, v in counter.most_common(len(counter)):
print(tokenizer.decode([k]), v)
seed_everything(Config.seed)
device = torch.device(Config.device)
start_epoch = 0
logging.info('Define Models')
model = build_model(Config).to(device)
tokenizer = Tokenizer.from_pretrained(Config.model_name)
logging.info('Define Loss and Optimizer')
criterion = LabelSmoothing(tokenizer.vocab_size,
pad_id=tokenizer.pad_token_id,
smoothing=Config.smoothing)
_opt = optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9)
optimizer = get_optimizer(_opt, factor=Config.factor, warmup=Config.warmup)
logging.info('Preparing training data')
if Config.use_pickle:
with open(f'{Config.pickle_path}', 'rb') as f:
train_data = pickle.load(f)
else:
train_data = make_train_data_from_txt(Config, tokenizer)
dataset = DialogDataset(train_data, tokenizer)
logging.info('Start Training')
for epoch in range(start_epoch, Config.n_epoch):
one_cycle(epoch, Config, model, optimizer, criterion,
BalancedDataLoader(dataset, tokenizer.pad_token_id),
tokenizer, device)
evaluate(Config, 'もう疲れたー', tokenizer, model, device)
