Transformers, Pytorch, Machine Learning, Deep Dives, Data Science.
epoch): model.train() losses = 0 acc = 0 history_loss = [] history_acc = [] with tqdm(loader, position=0, leave=True) as tepoch: for x, y in tepoch: tepoch.set_description(f"Epoch {epoch}") optimizer.zero_grad() logits = model(x, y[:, :-1]) loss = loss_fn(logits.contiguous().view(-1,