In this work, we discuss our models that were applied to the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection {cite{muhammad-etal-2025-semeval}}. We focused on the English data set of track A, which involves determining what emotions the reader of a snippet of text is feeling. We applied three different types of models that vary in their approaches and reported our findings on the task’s test set. We found that the performance of our models differed from each other, but neither of our models outperformed the task’s baseline model.