| 🥳 | |
| Training loop | |
| To keep track of your training progress, use the tqdm library to add a progress bar over the number of training steps: | |
| from tqdm.auto import tqdm | |
| progress_bar = tqdm(range(num_training_steps)) | |
| model.train() | |
| for epoch in range(num_epochs): | |
| for batch in train_dataloader: | |
| batch = {k: v.to(device) for k, v in batch.items()} | |
| outputs = model(**batch) | |
| loss = outputs.loss | |
| loss.backward() | |
| optimizer.step() | |
| lr_scheduler.step() | |
| optimizer.zero_grad() | |
| progress_bar.update(1) | |
| Evaluate | |
| Just like how you added an evaluation function to [Trainer], you need to do the same when you write your own training loop. |