BERT Fine-tuning › BERT Fine-tuning
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You have a pre-trained BERT model with 110 million parameters trained on billions of words. Now you need it to classify movie reviews, identify named entities, or answer questions about documents. How do you adapt this general language understanding to your specific task?
Fine-tuning is the answer. Rather than training from scratch, you take BERT's pre-trained weights and continue training on your task-specific data. The model already understands language; fine-tuning teaches it your particular task. This chapter covers the complete fine-tuning process: how to add task-specific heads for classification, sequence labeling, and question answering; how to set hyperparameters that balance learning speed against stability; and how to avoid catastrophic forgetting, where the model loses its pre-trained knowledge.