Hi Mahdi,
My guess is that with enough training data, the transformer model, and in particular its attention heads, will learn to recognise what they should be paying most attention to, i.e. which parts of the text are more important for the classification, and which parts are not that relevant, so this will happen implicitly, so long as the model has enough data. I’m not aware of a way to explicitly force BERT to weight some tokens more than others, however I’d be happy to be proven wrong by other contributors if this is the case.