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bert-base-uncased fine-tuned on dair-ai/emotion (16,000 training texts)
to classify short English texts into six emotions: sadness, joy, love, anger, fear, surprise.
Code and full evaluation: https://github.com/Cecile1015/emotion-mirror-cbs4702 (notebooks/06_bert.ipynb).
Full fine-tuning, [CLS] -> pooler -> dropout -> linear; class-weighted cross-entropy; AdamW, learning rate 5e-05
(selected from 2e-05, 3e-05, 5e-05 on validation macro-F1), 10% linear warm-up, batch size 32,
4 epochs; checkpoint selected by validation macro-F1 (epoch 3.0); seed 42.
| Metric | Value |
|---|---|
| Accuracy | 0.930 |
| Macro-F1 | 0.896 |
Per-class F1: sadness 0.969, joy 0.944, love 0.848, anger 0.941, fear 0.893, surprise 0.783.
Probabilities are calibrated by temperature scaling, T = 1.320 (stored in training_info.json;
apply softmax(logits / T)).
The dataset consists of English Twitter messages built around templates such as "i feel ...". The model recognises the emotion expressed in a text, not the writer's actual emotional state, and it is a course project, not a clinical or psychological instrument.
google-bert/bert-base-uncased