Emotions influence human behavior, speech, and expression, making their detection crucial in Natural Language Processing (NLP). While most prior research has focused on single-label emotion classification, real-world emotions are often multi-faceted. This paper describes our participation in SemEval-2025 Task 11, Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity). We employed BERT as a feature extractor with stacked GRUs, which resulted in better stability and convergence. Our system was evaluated across 19 languages for Track A and 9 languages for Track B.