Does finetuning BERT involving updating all of the parameters or just the final classification layer? Currently learning and reading about transformer models, I get that during the pretraining stage the BERT model is trained on a large corpus via MLM and NSP.  But during finetuning, for example trying to classify sentiment based on another text, are all of the BERT parameters (110M+ parameters + final classification layer) updated or just only final classification layers?  Couldn't find a concrete answer to this in the resources I've been looking at.
Thank you in advance.
Both approaches are reasonable. Updating the BERT weights will train for longer period of time, but should give more accurate results.