rmtariq/multilingual-emotion-classifier
Fine-tuned emotion classifier (7-class) for Malaysian higher-education feedback. Trained on MYUniDialectSentiment840 (840 samples, 14 dialects, 20 topics, 15 learning contexts), a hand-curated balanced corpus covering Standard Malay, 13 regional dialects, and Manglish code-switching.
Labels
happylovesurpriseno_clear_emotionsadnessangerfear
Held-out test metrics (n=147, stratified)
| split | accuracy | f1_macro |
|---|---|---|
| validation (n=105) | 0.9524 | 0.9440 |
| test (n=147) | 0.9796 | 0.9753 |
Intended use
Sentiment / emotion monitoring of student feedback for Malaysian higher-education institutions. Designed to handle code-switched, dialect-heavy and informal academic discourse.
Training details
- Base: previous revision of
rmtariq/multilingual-emotion-classifier - Optimizer: AdamW (lr=2e-5, weight_decay=0.01, warmup_ratio=0.1)
- Epochs: 5 with early stopping on validation macro-F1
- Batch size: 16 (train) / 32 (eval), max_length=128
- Hardware: Apple Silicon MPS
- Class-weighted cross-entropy (for emotion only)
Dataset
MYUniDialectSentiment840 — 840 samples, balanced on sentiment, stratified
70/12.5/17.5 train/val/test by sentiment-x-dialect.
Citation / authors
- Raja Mohd Tariqi B. Raja Lope Ahmad — Ts., Fiscal Digest Sdn. Bhd.
- Raja Qatrun Nada Bin Raja Mohd Tariqi — Master of Education, UKM