Identifying hate speech is a challenging specialization in the natural language processing field (NLP). Particularly in fields with differing linguistics, it becomes more demanding to construct a well-performing classifier for the betterment of the community. In this paper, we leveraged the performances of pre-trained models on the given hate speech detection dataset. By conducting a hyperparameter search, we computed the feasible setups for fine-tuning and trained effective classifiers that performed well in both subtasks in the HSD-2Lang 2024 contest.