Overview

Description

Fine-tuning involves taking a pretrained model and customizing it for a specific downstream task by training it further on a task-specific dataset. It adjusts the existing weights of the model to optimize performance for new, task-specific objectives.

• Task-Specific: Fine-tuning is explicitly tailored for a specific task (e.g., text classification, sentiment analysis, image segmentation).
• Requires Labeled Data: The task dataset often requires labeled examples for supervised learning.
• Adjusts Pretrained Weights: The pretrained weights of the model are updated during fine-tuning, enabling the model to specialize in the downstream task.
• Typically Large Data Needs: Depending on the model and task complexity, fine-tuning often requires a substantial amount of labeled task-specific data.

Usage

• When the pretrained model doesn't perform well on a specific task out of the box.
• If you have access to a moderately sized or large labeled dataset for your target task.
• For complex tasks that require significant task-specific adjustment of the original model weights.