Fine-tuning is the process of adapting a pre-trained NLP model to a specific task by training it further on a smaller, labeled dataset. The pre-trained model serves as a foundation, having already learned general language features such as grammar, syntax, and word relationships from large corpora. Fine-tuning adjusts the model weights to optimize performance on the target task.
For example, a pre-trained BERT model can be fine-tuned for sentiment analysis by training it on labeled reviews. During fine-tuning, the model’s layers are either fully updated or partially frozen, depending on the task and dataset size. The final layer is typically replaced with a task-specific head, such as a classification layer for sentiment prediction.
Fine-tuning enables developers to achieve high performance without requiring extensive resources or large datasets. Libraries like Hugging Face Transformers provide APIs to fine-tune models easily. This approach has revolutionized NLP, making it possible to apply state-of-the-art models to tasks like text classification, translation, and summarization with minimal effort.