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+ 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. |