Remove columns before training


I see in many examples that we remove unnecessary columns from the dataset, before training.
Is it mandatory? I guess Trainer will take the columns produced by the tokenizer anyway (together with the “labels” columns).
Is it to save some IOs? I guess transformer does not load unnecessary columns, but not sure then, why in examples, we remove them.

Sorry, very beginner question here :slight_smile: