Hello! Thank you for your question. Regarding the first part of the question, I suggest reading this 4 paper as suggested here 4 and you will find a lot of details about how the model was trained (from parameters to task) or references to other papers where said info is contained. I suggest that you use the checkpoint loading functionality of the framework they used to implement to see if they saved the hyperparameters with the checkpoint or not. For any specific details about training and data pre-processing that are missing from their paper and cannot be inferred from references, it’s always worth contacting the authors!
You will have to explore what learning parameters make sense for your task and data, you don’t need to train with the parameters they trained with. So, in a way, their training configuration does not impact all that much what you do. You just need to find a good set of parameters that work well for your task.
Depending on your task, you might want to use the trainer and the existing data loaders or implement your own. So I can’t give any answers there. If you add your tokens to the vocabulary, I would expect the tokenizer converts them to subword units the model has learned in training. So I don’t expect there is any additional step you need to do in that regard.