I believe the tokenizer argument is only required in case you haven’t batched the data yourself:

If provided, will be used to automatically pad the inputs the maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model.

I do have a notebook where I’ve updated the outdated blog post of @patrickvonplaten: Google Colab 3.
I believe you just need to update the process_data_to_model_inputs function, namely it should use BertTokenizer to prepare the inputs and RobertaTokenizer to prepare the labels. No tokenizer should be passed to the Seq2SeqTrainer.