Hi,
I am  using a fine-tuned bert model for text classification and, of course, I am getting very high accuracy scores. However, this masks a more nuanced reality when I look at the precision/recall metrics. See, for instance:
              precision    recall  f1-score   support

           0      0.962     0.976     0.969     75532
           1      0.516     0.395     0.448      4820

    accuracy                          0.941     80352
   macro avg      0.739     0.686     0.708     80352
weighted avg      0.935     0.941     0.938     80352

My question is: for a given transformers architecture (say Bert) do we know what we can do to improve either precision or recall (I know there is a tradeoff between the two). For instance is there evidence that oversampling the most infrequent class could improve precision? What can be done here, beyond having more training data?
Thanks!