Finetuning

Finetuning is a machine learning technique where a pre-trained model is further trained on a smaller, often more specific dataset.

It is used when there is a need to adapt a general model to a specific task or domain. Finetuning is commonly applied in scenarios where a pre-trained model, needs to be tailored to a particular application. The technique works by taking a model that has already been trained on a large unspecific dataset and continuing the training process with a smaller, more curated dataset that contains dedicated knowledge or information.

For example, a model pre-trained on a large dataset of general images can be fine-tuned to recognize specific objects in medical images. In natural language processing, a model like GPT or BERT, pre-trained on a large corpus of text, can be fine-tuned for specific tasks such as sentiment analysis or question answering.

Finetuning is important because it allows for the efficient adaptation of pre-trained models to new tasks, reducing the amount of data and computational resources required. It is a powerful approach in machine learning, enabling the reuse of existing models and knowledge to tackle new challenges effectively.