Hope speech detection in low-resource, code-mixed languages presents a genuine challenge for natural language processing. Tulu, a Dravidian language spoken along the coastal regions of Karnataka and Kerala, is one such language where social media content is deeply code-mixed, blending Tulu, Kannada script, and English within a single comment. Two classification tasks are addressed: a four-class coarse-grained setting (Track 1) and a five-class fine-grained setting (Track 2). XLM-RoBERTa, a cross-lingual transformer pre-trained on more than 100 languages, is fine-tuned on the task-provided datasets using Google Colab with an NVIDIA T4 GPU. The system achieves a Macro F1-score of 0.34 on Track 1 and 0.19 on Track 2 on the official Codabench evaluation, establishing the first transformer-based baseline for hope speech classification in Tulu.

Detecting abusive language in Tamil social media is a genuinely difficult problem. The language is morphologically rich, speakers routinely mix Tamil with English, and informal romanised Tamil is common enough to confuse models trained primarily on formal text. This work presents a system for binary classification of Tamil comments into Abusive and Non-Abusive categories, submitted to the DravidianLangTech@ACL 2026 shared task. MuRIL, a BERT-based encoder pre-trained on 17 Indian languages and their transliterated equivalents, is fine-tuned, and it is shown that this Indian-language-specific pre-training provides a meaningful advantage over generic multilingual baselines. The system achieves a macro-averaged F1 of 0.83 on the validation set, compared to 0.79 for XLM-RoBERTa and 0.77 for mBERT under identical training conditions, establishing a strong transformer-based baseline for abusive language detection in code-mixed Tamil.