What are the best practices for fine-tuning pre-trained models?

What are the best practices for fine-tuning pre-trained models?

Fine-tuning pre-trained models involves several best practices, such as using a smaller learning rate, freezing some layers, and ensuring a diverse dataset.

Why is it important to use a smaller learning rate?

A smaller learning rate helps prevent drastic changes to the weights of the pre-trained model, allowing it to adapt more smoothly to the new task.

How do I decide which layers to freeze?

Typically, you should freeze the earlier layers that capture general features and fine-tune the later layers that are more task-specific.

Can you recommend any tools for fine-tuning?

Sure! Popular tools include Hugging Face's Transformers library, TensorFlow, and PyTorch, which all provide great support for fine-tuning.

What kind of dataset should I use for fine-tuning?

You should use a dataset that is relevant to your specific task and diverse enough to cover various scenarios the model might encounter.