I have trained my BERT model(bert-base-cased) to detect toxic comments. I used the Toxic Comment Classification Challenge dataset from the Kaggle. My accuracy is 98% and the AUROC for various sub-classes is above 90%. However, my Precision, Recall, and F1 score is less. The scores are shown in the image Evaluation Scores. The dataset is highly imbalanced. The ratio of clean comments is way higher than the toxic comments. Any suggestions to improve the evaluation scores?
Here's the final score
precision recall f1-score support
micro avg 0.61 0.85 0.71 1743
macro avg 0.56 0.69 0.61 1743
weighted avg 0.64 0.85 0.72 1743
samples avg 0.08 0.09 0.08 1743