| # Model Card for German Hate Speech Classifier | |
| ## Model Details | |
| ### Introduction | |
| This model was developed to explore the potential of German language models in multi-class classification of hate speech in German online journals. It is a fine-tuned version of the GBERT model from (Chan, Schweter, and Möller, 2020). | |
| ### Dataset | |
| The dataset used for training is a consolidation of three pre-existing German hate speech datasets: | |
| - **RP (Assenmacher et al., 2021)** | |
| - **DeTox (Demus et al., 2022)** | |
| - **Twitter dataset (Glasenbach, 2022)** | |
| The combined dataset underwent cleaning to minimize biases and remove redundant data. | |
| ## Performance | |
| Our experiments delivered promising results, with the model reliably classifying comments into: | |
| - **No Hate Speech** | |
| - **Other Hate Speech (Threat, Insult, Profanity)** | |
| - **Political Hate Speech** | |
| - **Racist Hate Speech** | |
| - **Sexist Hate Speech** | |
| The model achieved a macro F1-score of 0.775. However, to further reduce misclassifications, improvements are essential. Short comments are overproportionally classified as Sexist Hate Speech. |