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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. |