  This report provide a detailed description of the method that we proposed in
the TRAC-2024 Offline Harm Potential dentification which encloses two
sub-tasks. The investigation utilized a rich dataset comprised of social media
comments in several Indian languages, annotated with precision by expert judges
to capture the nuanced implications for offline context harm. The objective
assigned to the participants was to design algorithms capable of accurately
assessing the likelihood of harm in given situations and identifying the most
likely target(s) of offline harm. Our approach ranked second in two separate
tracks, with F1 values of 0.73 and 0.96 respectively. Our method principally
involved selecting pretrained models for finetuning, incorporating contrastive
learning techniques, and culminating in an ensemble approach for the test set.
