  Online presence on social media platforms such as Facebook and Twitter has
become a daily habit for internet users. Despite the vast amount of services
the platforms offer for their users, users suffer from cyber-bullying, which
further leads to mental abuse and may escalate to cause physical harm to
individuals or targeted groups. In this paper, we present our submission to the
Arabic Hate Speech 2022 Shared Task Workshop (OSACT5 2022) using the associated
Arabic Twitter dataset. The shared task consists of 3 sub-tasks, sub-task A
focuses on detecting whether the tweet is offensive or not. Then, For offensive
Tweets, sub-task B focuses on detecting whether the tweet is hate speech or
not. Finally, For hate speech Tweets, sub-task C focuses on detecting the
fine-grained type of hate speech among six different classes. Transformer
models proved their efficiency in classification tasks, but with the problem of
over-fitting when fine-tuned on a small or an imbalanced dataset. We overcome
this limitation by investigating multiple training paradigms such as
Contrastive learning and Multi-task learning along with Classification
fine-tuning and an ensemble of our top 5 performers. Our proposed solution
achieved 0.841, 0.817, and 0.476 macro F1-average in sub-tasks A, B, and C
respectively.
