  Hate speech detection within a cross-lingual setting represents a paramount
area of interest for all medium and large-scale online platforms. Failing to
properly address this issue on a global scale has already led over time to
morally questionable real-life events, human deaths, and the perpetuation of
hate itself. This paper illustrates the capabilities of fine-tuned altered
multi-lingual Transformer models (mBERT, XLM-RoBERTa) regarding this crucial
social data science task with cross-lingual training from English to French,
vice-versa and each language on its own, including sections about iterative
improvement and comparative error analysis.
