chunk_content:    total_loss = 0
      for step, batch in enumerate(tqdm(train_dataloader)):
          batch = {k: v.to(device) for k, v in batch.items()}
          outputs = model(**batch)
          loss = outputs.loss
          total_loss += loss.detach().float()
          loss.backward()
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
  
      model.eval()
      eval_loss = 0
      eval_preds = []
      for step, batch in enumerate(tqdm(eval_d