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The influence of social media is one of the most dominating characteristics of the current era, and this has led cyberbullying to grow into a more serious social issue. As a result, automated cyberbullying detection systems need to be an integral part of almost all social media platforms. Past studies on this domain have primarily focused on hand-picked features and traditional machine learning approaches for cyberbullying detection from user comments on social media. Recently, transformers have been proved to be quite effective in various language-related tasks; however, their effectiveness has not been extensively explored in this particular domain. In this study, we evaluate the individual performance of several well-known transformer-based architectures and aim to contribute to the development of automated cyberbullying detection systems by proposing our own transformer-based ensemble framework. Our …