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XLM-RoBERTa fine-tuned for multilingual toxicity detection across multiple languages. Achieves Mean ROC-AUC of 0.9918 with LIME-based explainability and an interactive Gradio demo.
Built an NLP-powered model that detects toxic comments across multiple languages in real time, helping create safer online spaces by flagging harmful content with high accuracy and scalability.
An edge-optimized, multilingual NLP architecture delivering 99%+ accuracy via lightweight Soft-Voting Ensembles, completely bypassing the latency, API costs, and hardware bottlenecks of heavy LLMs.
This project focuses on building a robust Natural Language Processing (NLP) model to classify news articles as Real or Fake.
