chunk_content: _epoch_loss = eval_loss / len(eval_dataloader)
      eval_ppl = torch.exp(eval_epoch_loss)
      train_epoch_loss = total_loss / len(train_dataloader)
      train_ppl = torch.exp(train_epoch_loss)
      print(f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}")
  Let’s see how well the model performs on the validation set:
  
  
  	Copied
  correct = 0
  total = 0
  for pred, true in zip(eval_preds, dataset["validation"]["text_label"]):
      