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.

该研究提出了一种方法，使用 TRAC-2024 离线危害潜力识别，它包括两个子任务。研究利用了一个丰富的数据集，包含几种印度语言的社交媒体评论，并由专家评分进行注释，以捕捉离线背景危害的微妙含义。我们的方法在两个不同的任务中排名第二，分别具有 0.73 和 0.96 的 F1 值。我们的方法主要涉及选择预训练模型进行微调，结合对应学习技术，并最终通过测试集进行集成。