chunked/nltk_chunking/_accelerate/chunk_8.txt

+ from accelerate import Accelerator
  from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler

accelerator = Accelerator()

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
  optimizer = AdamW(model.parameters(), lr=3e-5)

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model.to(device)

train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(

train_dataloader, eval_dataloader, model, optimizer
)

num_epochs = 3
  num_training_steps = num_epochs * len(train_dataloader)
  lr_scheduler = get_scheduler(
      "linear",
      optimizer=optimizer,
      num_warmup_steps=0,
      num_training_steps=num_training_steps
  )
progress_bar = tqdm(range(num_training_steps))
model.train()
  for epoch in range(num_epochs):
      for batch in train_dataloader:

          outputs = model(**batch)
          loss = outputs.loss

+         accelerator.backward(loss)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      progress_bar.update(1)

Train
Once you've added the relevant lines of code, launch your training in a script or a notebook like Colaboratory.