First a model absorbs generic lock-and-harbor language from a huge unlabeled pile; later a small labeled set of “delay / no delay” notes adapts it to one desk’s ticket codes. What are those two stages?
Select an answer to reveal the explanation.
Short Explanation
First the model drinks a huge unlabeled pile of lock-and-harbor language. Later a small labeled set of delay notes teaches it one desk’s ticket codes. That is pretraining, then fine-tuning. Swap the names, or call both inference or job queues, and the stages are upside down.
Full Explanation
Pretraining is the large general (often self-supervised) stage. Fine-tuning adapts the model to a smaller specialized or labeled task. Swapping those names, or calling both inference or job queues, is wrong.