Dostrajanie

Dostrajanie, often translated as fine-tuning, is a machine learning technique that involves adapting a pre-trained model to a new, specific task. Instead of training a model from scratch, which can be computationally expensive and require vast amounts of data, fine-tuning leverages the knowledge already learned by a model on a large, general dataset.

The process typically begins with a model that has been trained on a massive corpus of data, such as text from the internet or a large collection of images. This pre-training allows the model to develop a robust understanding of general patterns, features, and relationships within the data domain. For example, a language model pre-trained on general text might learn grammar, syntax, and common word meanings.

Once pre-trained, the model is then subjected to a smaller, task-specific dataset. During fine-tuning, the model's parameters, which were previously optimized for the general task, are further adjusted or "tuned" to better perform the new, specialized objective. This often involves training for fewer epochs and with a lower learning rate compared to initial pre-training. The layers of the pre-trained model can be updated entirely, or specific layers might be frozen to preserve general knowledge while only adapting the final layers for the new task.

Fine-tuning is widely used in natural language processing (NLP) and computer vision. In NLP, pre-trained models like BERT or GPT can be fine-tuned for tasks such as sentiment analysis, question answering, or text summarization. In computer vision, models trained on ImageNet can be fine-tuned for specific object detection or image classification problems. This approach significantly reduces the data and computational resources needed for many practical applications.