Hi Para, I’m afraid there is no magic bullet in HF that can help solve this problem. It’s a common ML challenge and as such the standard approaches apply: (1) Under-sampling: Randomly delete records from the over-represented classes (2) Over-sampling: Duplicate records in the under-represented classes (3) Synthetic sampling/data augmentation: In NLP, there are actually quite some interesting techniques with which you can augment your data in the under-represented classes to increase record count. Check out this library: GitHub - makcedward/nlpaug: Data augmentation for NLP
Hope that helps, let me know if any questions.
Cheers
Heiko