The Faux-Hate task looks at two big issues:misinformation and hate speech. It focuses onHindi-English social media posts. This papershares our methods for both parts of the task.For Task A, we built a special model based onXLM-RoBERTa. It has features that help usspot both fake news and hate speech at the sametime. For Task B, we wanted to identify whothe hate is aimed at (like individuals or groups)and how severe it is (high, medium, low). So,we added different tools to our model for thiskind of sorting. To get ready for all this, wecarefully cleaned the data, especially dealingwith mixing languages and different spellings.In Task A, our results show that our model canclearly tell the difference between fake and realstories, as well as between hate and non-hatespeech. For Task B, it does a good job withidentifying targets and severity levels, givingstrong predictions for multiple labels. Overall,these results show that cross-lingual models,combined with specific tweaks, can really helptackle complex text classification in languageswith fewer resources.