Hi, thanks for answering.
Ah, so it’s more like restarting the training but from a check-point, not actually continuing entirely from where you left it, at least considering the learning rate’s values. I suppose I could continue the training by setting a very low learning rate, to approximate the values it would have were it to continue training normally from epoch 40. Regarding the scheduler, you are right, it is the linear decay but I think it also has optional warm-up steps, in which case it becomes OneCycleLR.
Also, considering the argument output_dir of the new TraininbArguments objects I will define to restart training: do I pass any path I want there? I can use a different path from the previous one, or the old one. If I use the old one, is it usual to use overwrite_output_dir=True so that it overwrites the old checkpoint?
Thanks in advance.