What are the best practices for using pre-trained models in AI?

What are the best practices for using pre-trained models in AI?

When using pre-trained models, it's important to fine-tune them on your specific dataset, ensure data quality, and understand the model's limitations.

How do I fine-tune a pre-trained model?

To fine-tune a pre-trained model, you typically need to adjust the model's weights using your dataset while keeping the original architecture.

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

Use high-quality, relevant data that closely resembles the task you want the model to perform. This helps improve accuracy.

Are there any specific frameworks I should use?

Popular frameworks include TensorFlow, PyTorch, and Hugging Face's Transformers, which provide tools for fine-tuning.

What are the common pitfalls to avoid?

Common pitfalls include overfitting, using too little data, and not validating the model's performance on unseen data.