How to reset dataloader or global_step for continued training?
I have finetuned MT5-base model for machine translation task on a corpus `A` and I would want to swap the dataset and continue training on corpus `B` (this is not finetuning but a form of continued training), and force the dataloader to start afresh instead of continuing from the point where it stopped previously on corpus `A`. Currently, to do continue training, I used `resume_from_checkpoint` to provide the checkpoint of the MT model for corpus `A` and provide the path to the new data.
Is there an argument or parameter to reset data loader? I noticed there is a  `ignore_data_skip` parameter but this does not solve the issue.


if the continued training stops on `B` due to GPU limit, how do I ensure that when I try to continue training on `B`, it has the right data index to start from?
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