MMD2 — BRIGHTER Multilingual Emotion Classifiers
Fine-tuned models for the BRIGHTER benchmark (SemEval-2025 Task 11):
multilingual multi-label emotion classification.
Submitted as part of the Mining Media Data 2 course project.
Task
- Input: short text (tweet / comment) in one of 28 languages
- Output: one or more emotion labels from {anger, disgust, fear, joy, sadness, surprise}
- Metric: macro-F1 over emotion labels
Models in this repository
| Directory | Base model |
|---|
mbert/ | google-bert/bert-base-multilingual-cased |
qwen3.5-0.8b/ | Qwen/Qwen3.5-0.8B |
qwen3.5-0.8b-chinese-ft/ | Qwen/Qwen3.5-0.8B |
qwen3.5-2b/ | Qwen/Qwen3.5-2B |
qwen3.5-2b-chinese-ft/ | Qwen/Qwen3.5-2B |
qwen3.5-4b/ | Qwen/Qwen3.5-4B |
qwen3.5-4b-chinese-ft/ | Qwen/Qwen3.5-4B |
Dataset
