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.