What are the best practices for training transformer models?

What are the best practices for training transformer models?

To train transformer models effectively, consider using a large and diverse dataset, optimizing hyperparameters, and employing techniques like transfer learning.

How important is the dataset size for training?

Dataset size is crucial; larger datasets help the model generalize better and capture more patterns.

What hyperparameters should I focus on?

Key hyperparameters include learning rate, batch size, and the number of attention heads. Tuning these can significantly impact performance.

Can you explain transfer learning in this context?

Transfer learning involves pre-training a model on a large dataset and then fine-tuning it on a smaller, task-specific dataset, which can save time and resources.

What are some common pitfalls to avoid?

Common pitfalls include overfitting, not using enough data augmentation, and neglecting to monitor training metrics.