Fine-Tuning BERT for Text Classification

Fine-tuning BERT for text classification involves adapting a pre-trained model to a specific use case with additional training. This method significantly reduces training costs and improves performance. The post walks through fine-tuning BERT to classify phishing URLs using the Hugging Face Transformers library, covering key tasks like tokenizing data, freezing model parameters, and setting up training. The provided Python code demonstrates the entire process, and the resulting model is computationally efficient enough to run on consumer hardware.