Example Concept: Fine-tuning a transformer model for text classification typically involves the following steps: First, load a pre-trained transformer model and tokenizer. Then, prepare your dataset by tokenizing the text and creating input tensors. Next, add a classification head to the model, which is a simple feed-forward neural network layer. Fine-tune the model on your dataset by adjusting the learning rate and other hyperparameters, often using a smaller learning rate than initial training. Finally, validate the model's performance on a separate validation set to ensure it generalizes well.