ประเมินและบันทึกโมเดลของคุณ

A trained model is only useful if it generalizes. So your next job is to evaluate it on data it never saw during training.

The Trainer can score your validation set in one call and return the metrics you configured, like accuracy and loss.

Accuracy can mislead on skewed data. For uneven classes, lean on F1 so both precision and recall count.

The evaluate library wraps common scores, so you compute F1 or accuracy without writing the math by hand.

Keep a separate test set you touch only once. It gives an honest final estimate, free of tuning bias.

Persist the fine-tuned weights to disk so you can reload them anytime without training again. This saves the model and tokenizer.

Loading is the mirror of saving: point from_pretrained at your folder and the full model comes back ready to predict.

Drop your saved model into a pipeline for instant, simple inference on fresh text without any manual tokenizing.

One push uploads your model to the Hugging Face Hub, so teammates load it by name from anywhere.

The Hub tracks revisions like git, so each checkpoint stays reproducible and you can always roll back.

A model card records your data, metrics, and limits. It helps others trust and reuse what you built. 📋

Why keep a separate test set you evaluate only once?

Evaluate on unseen data with the right metric, save the model and tokenizer, then reload or push it to the Hub to share. ✅