Thymos-6D
Published model bundle: 0x4C57/Thymos-6D
Thymos is a six-dimensional text affect model. It maps a message to a bounded continuous state rather than a single emotion category:
[valence, arousal, dominance, connectionNeed, openness, confidence]
The model fine-tunes the full Qwen/Qwen3-Embedding-0.6B encoder with a
1024 -> 256 -> 6 sigmoid head. The exported model.pt is a TorchScript
bundle for DJL integration; text_encoder/ and head.pt are included for
Python inference and reproducibility.
Results
The held-out test split contains 819 records. Mean absolute error is
0.078755 across the six dimensions.
Per-dimension MAE:
| Dimension | MAE |
|---|---|
| valence | 0.05160 |
| arousal | 0.06291 |
| dominance | 0.05799 |
| connectionNeed | 0.06470 |
| openness | 0.07713 |
| confidence | 0.15821 |
The training corpus was generated and reviewed synthetically. These metrics
are not a substitute for evaluation on consented, representative human data.
The model is not a clinical or mental-health diagnostic tool. Sarcasm,
negation, code-switching, cultural context and ambiguous short messages may
be unreliable. Treat confidence as an uncertainty signal, not a probability
that the prediction is correct.
Python inference
python -m pip install -r requirements.txt
python thymos_inference.py --model model --text "别担心，我只是有点累"
Re-training
The raw training corpus is intentionally not included. Provide a local JSONL
corpus with the fields sampleId, text, and the six output names, then run:
python scripts/train-user-affect-model.py --corpus path/to/corpus.jsonl --output model
Training requires CUDA and BF16. CPU fallback is disabled by design.
License and attribution
The repository code and Thymos model artifacts are licensed under AGPL-3.0-or-later. The model is a fine-tuned derivative of Qwen3-Embedding-0.6B; upstream copyright, attribution, license notices and terms continue to apply. See the Qwen model card before redistribution or commercial use.