In this paper, we describe our system devel-oped for participation in SemEval-2025 Task11: Bridging the Gap in Text-Based EmotionDetection. We compare three approaches formultilingual, multi-label emotion classification:XLM-R, an ensemble of models (XLM-5), anda prompt-based approach. We evaluate the per-formance of these models across a diverse setof languages, ranging from high-resource tolow-resource languages