With the rise of social media platforms, we need to ensure that all users have a secure online experience by eliminating and identifying offensive language and hate speech. Furthermore, detecting such content is challenging, particularly in the Arabic language, due to a number of challenges and limitations. In general, one of the most challenging issues in real-world datasets is long-tailed data distribution. We report our submission to the Offensive Language and hate-speech Detection shared task organized with the 5th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT5); in our approach, we focused on how to overcome such a problem by experimenting with alternative loss functions rather than using the traditional weighted cross-entropy loss. Finally, we evaluated various pre-trained deep learning models using the suggested loss functions to determine the optimal model. On the development and test sets, our final model achieved 86.97% and 85.17%, respectively.