Скачать или смотреть The Concept of Fine-tuning in NLP

@genaiexp Fine-tuning is a crucial step in transfer learning, involving the adaptation of a pre-trained model to a specific task. In NLP, fine-tuning allows you to adjust the model's parameters based on your task-specific dataset, enhancing its performance. This process begins with selecting a pre-trained model, followed by training it on your dataset while preserving the learned knowledge. Fine-tuning enables the model to understand task-specific nuances, such as domain-specific vocabulary and context. However, challenges like overfitting can arise, requiring strategies such as using smaller learning rates and regularization techniques to ensure optimal performance without losing the benefits of pre-training.