from transformers import AdamW from torch.optim.lr_scheduler import StepLR # Define the model and optimizer model = MyModel() optimizer = AdamW(model.parameters(), lr=1e-5, weight_decay=0.01) # Define the step function scheduler = StepLR(optimizer, step_size=10, gamma=0.1) # Train the model with a loop over epochs for epoch in range(num_epochs): for batch in data_loader: # Compute the loss and gradients loss = model(batch) loss.backward() # Update the parameters optimizer.step() scheduler.step() optimizer.zero_grad()In this example, the AdamW optimizer is used to optimize a deep learning model, with a learning rate of 1e-5 and weight decay of 0.01. The StepLR function is used to decrease the learning rate by a factor of 0.1 every 10 steps. This code example uses the PyTorch library, with the AdamW optimizer and StepLR scheduler coming from the Transformers package. In summary, the AdamW optimizer with StepLR scheduler is a useful tool for training deep learning models with natural language processing tasks. It can help prevent overfitting and improve model generalization, and is commonly used in the Transformers package for PyTorch.