Fine-Tune a Text Classifier

Why fine-tune a classifier

For high-volume, latency-sensitive classification, a small fine-tuned model is cheaper and faster than calling an LLM per request — and often more accurate on your specific labels.

from transformers import AutoModelForSequenceClassification, Trainer
model = AutoModelForSequenceClassification.from_pretrained(
    "distilbert-base-uncased", num_labels=4)
Trainer(model=model, args=args, train_dataset=ds).train()

The workflow

Label a few hundred to a few thousand examples (an LLM can help bootstrap them), fine-tune a small encoder like DistilBERT, and evaluate on a held-out set. You get a fast, self-hosted classifier you fully control.