We tackle the challenge of multi-label emotion detection in short texts, focusing on SemEval-2025 Task 11 Track A. Our approach, RoEmo, combines generative and discriminative models in an ensemble strategy to classify texts into five emotions: anger, fear, joy, sadness, and surprise.The generative model, instruction-finetuned on emotion detection datasets, undergoes additional fine-tuning on the SemEval-2025 Task 11 Track A dataset to enhance its performance for this specific task. Meanwhile, the discriminative model, based on binary classification, offers a straightforward yet effective approach to classification.We review recent advancements in multi-label emotion detection and analyze the task dataset. Our results show that RoEmo ranks among the top-performing systems, demonstrating high accuracy and reliability.