Loss Increases But Metrics Get Better?
HiI was experimenting with theToken Classification Example Notebookand encountered something odd.This notebook fine-tunes DistilBERT on the WNUT-17 dataset. I have increased the original 2 training epochs up to 10 and the results are as follows:image913×518 43.3 KBAs can be seen from the picture above, during the fine-tuning process thevalidationloss increases (probably a case of over-fitting) while the metrics improve.Can someone explain this phenomenon?Thanks!