Preprocessing for Text Classification in Transformer Models (BERT variants)
A quick experiment you can do is to once do the preprocessing steps that you usually do and then feed it to the model and get the results. And once feed the dataset as it is to the model to compare the difference.
In my experience doing the preprocessing won't make any difference, based on the dataset it gave me 1 more or less percent difference in accuracy (not a considerable change).
When these models are trained, no preprocessing is done, as they want to learn the context of all sorts of sentences.
A reason that your results are not good enough might because of your label distribution. Most of the time the datasets are populated with only one or two labels and other labels are only a small portion of the dataset. If that's the case you might want to look into oversampling solutions.