In this paper, we present our solution for the shared task of GermEval 2024 GerMS-Detect. The joint task consists of two subtasks that we address in our solution. The texts in question may contain instances of sexism or misogyny and have been annotated in a multi-class classi- fication setting. From this setting, two tasks are derived that require different binary or multi- class classifications. We propose an ensem- ble method using multiple sequence classifica- tion models that can be applied to both sub- tasks. With respect to Subtask 1, our approach achieves an average F1 score of 0.641, and with respect to Subtask 2, our approach achieves an average Jensen-Shannon divergence of 0.354. The code is available at the following link: https://github.com/fmaoro/germeval24    «

In this paper, we present our solution for the shared task of GermEval 2024 GerMS-Detect. The joint task consists of two subtasks that we address in our solution. The texts in question may contain instances of sexism or misogyny and have been annotated in a multi-class classi- fication setting. From this setting, two tasks are derived that require different binary or multi- class classifications. We propose an ensem- ble method using multiple sequence classifica- tion models that can be applied...    »