Fine-tune a text classifier with a held-out evaluation set

Fine-tuning adapts model parameters to a labelled task. Architecture, learning rate, epoch and threshold choices consume validation information. The test set should remain closed until that entire policy is fixed.

A runnable checkpoint-selection proxy

The local NLP lab incrementally updates a linear log-loss classifier for 15 epochs on training TF-IDF. This is an executable demonstration of checkpoint roles, not transformer fine-tuning.

from nlp_cases import finetune_case

result = finetune_case()
assert result["best_epoch"] < result["epochs"]
assert len(result["trajectory"]) == 15
print(result["best_epoch"], result["held_out_test_accuracy"])

The easy template task reaches 100% validation accuracy after epoch 0 and never improves. The epoch-0 checkpoint is retained and scores 100% on the 30-row test. Continuing through epoch 14 adds computation without validation evidence.

The result is intentionally limited: repeated phrases, linear model, no transformer runtime and no real tickets. It demonstrates that a training loop must retain the selected checkpoint rather than assume the final epoch is best.

Fix data and model choices

Split by conversation, source and time as the use case requires. Fit tokenizer or vocabulary on training. Set seed, maximum length, batch size, optimizer, learning-rate schedule, evaluation cadence, patience and primary metric. Save a model card with package and base-model revision for transformer work.

Use macro F1 and per-class results where labels are imbalanced. Track difficult language, length and mixed-intent slices. If several checkpoints tie, prefer the earliest or simplest according to a predeclared rule.

Compare against the baseline

Run TF-IDF and the fine-tuned candidate on identical IDs. Report improvement, variability, latency and artifact size. A transformer that ties the baseline may still be unjustified for a small routing task.

The Data Science course links fine-tuning syntax with evaluation boundaries and reproducible checkpoints.

Exercise

Fine-tune a reviewed model in an isolated environment on group-disjoint data. Record validation macro F1 every epoch, restore the best checkpoint and compare once with TF-IDF on test and latency.

Continue learning

This article is part of the NLP and text analytics sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Handle long documents with explicit truncation rules.
• Continue with Compare a transformer with a linear baseline.

Reference: Hugging Face text classification task guide.