What is the purpose of fine-tuning a pre-trained model?

Fine-tuning a pre-trained model involves taking a model that has already been trained on a large dataset and continuing the training process using a smaller, more specific dataset. This process allows the model to adapt to particular nuances and features relevant to the new dataset, enhancing its performance in that specific context while leveraging the generalized knowledge it gained from the initial training.

By focusing on a specific dataset during fine-tuning, the model can learn patterns that are particularly important for the task it is being adapted to, which might not have been present in the larger dataset. This approach is efficient because it saves time and computational resources compared to training a model from scratch, and it often yields better performance on specialized tasks.

Utilizing this technique effectively can lead to improved accuracy and relevance in the model's predictions or classifications for the new data it will encounter in practice.