Capstone Lesson 38: Classifier Fine-Tuning by Head Swap

Track B's first capstone. A pretrained language model is a stack of self-attention blocks ending in a token-prediction head. When you want spam vs ham, the head is wrong but the body is mostly right. This lesson rips the head off, glues a two-class linear layer onto the pooled representation, and trains the classifier two different ways: final-layer only, and full fine-tuning. The eval is precision, recall, and F1 on a held-out split. You learn what each strategy buys you and what it costs.