Is it good practice to use SMOTE when you have a data set that has imbalanced classes when using BERT model for text classification?
I don't know about any specific recommendation related to BERT, but my general advice is this:

Do not to systematically use oversampling when the data is imbalanced, at least not before specifically identifying performance issues caused by the imbalance. I see many questions here on DataScienceSE about solving problems which are caused by using oversampling blindly (and often wrongly).
In general resampling doesn't work well with text data, because language diversity cannot be simulated this way. There is a high risk to obtain an overfit model.