Summary: Fine-tune a pretrained model

Fine-tuning is the cheap part from lesson 1, made concrete: take a pretrained model, continue training it on a task-specific dataset for a few minutes, and it learns a task it could not do before. The transformers Trainer handles the hard machinery. You prepare data (a data collator does dynamic padding per batch), load the model with a task head (which triggers an expected warning: the pretraining head is dropped and a random one added), set hyperparameters in a single TrainingArguments object, assemble the Trainer, and call trainer.train(). Crucially, a falling training loss is not proof of quality, so you add an eval_strategy and a compute_metrics function to measure on held-out data. This is the scan version; the lesson runs the whole loop on the MRPC dataset.

Core ideas

• Fine-tuning continues training a pretrained model on your data. It is the step you will use far more than pre-training, and it turns a generic model into one shaped for your task.
• A data collator does dynamic padding. DataCollatorWithPadding pads each batch to its own longest example, not the whole dataset to one length, saving compute.
• The head-swap warning is expected. Loading AutoModelForSequenceClassification on a base model discards the pretraining head and adds a randomly initialized task head. The warning means the setup is correct; training makes the new head useful.
• TrainingArguments is the one config object. It holds every hyperparameter; only an output directory is required. Defaults work for a basic run.
• Trainer assembles the pieces and runs the loop. Model, args, datasets, collator, tokenizer (processing_class), optionally compute_metrics; trainer.train() starts it.
• Evaluation needs more than loss. Set an eval_strategy and a compute_metrics function: turn logits into predictions with argmax, then score with the evaluate library to get accuracy and F1 on held-out data.

What changes for you

This is the lesson that moves you from using models to shaping them. Most applied AI work lives right here: a strong base model plus a modest labeled dataset, fine-tuned for a specific job. The Trainer is what makes that practical, because the genuinely hard parts (a correct training loop, evaluation plumbing, mixed precision, multi-GPU support) are handled, and the parts you write (which data, which metric, which hyperparameters) are exactly the parts that encode your problem. The habit to carry forward is the evaluation discipline: never trust a falling training loss as evidence of a good model, always measure on data the model has not seen. The next lesson takes the model you just fine-tuned and shares it on the Hub, closing Phase 1’s run-adapt-share arc.

Pre-training builds a model that knows language; fine-tuning, in a few minutes and a few lines, makes it know your task.