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ukr-detect/ukr-emotions-classifier, used by the speedbrain injection-engine / Persona Forge to detect the user's emotion per chat turn and shift the character's Emotion Area (resonance). Label set: ukr7, folded into the canonical 7-emotion space (anger, disgust, fear, joy, neutral, sadness, surprise).python -m injection_pro_persona.fetch ukmodel_quantized.onnx / model.onnx + tokenizer.json with onnxruntime (CPUExecutionProvider).ukr-detect/ukr-emotions-classifier; this repo only contains a format conversion (ONNX export, int8 quantization). The license of the base model applies unchanged and is included in this repo (see LICENSE). For RAIL-type licenses the use-case restrictions of Attachment A bind every downstream user of this model as well.1@inproceedings{dementieva-etal-2025-emobench,
2 title = "{E}mo{B}ench-{UA}: A Benchmark Dataset for Emotion Detection in {U}krainian",
3 author = "Dementieva, Daryna and
4 Babakov, Nikolay and
5 Fraser, Alexander",
6 editor = "Christodoulopoulos, Christos and
7 Chakraborty, Tanmoy and
8 Rose, Carolyn and
9 Peng, Violet",
10 booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
11 month = nov,
12 year = "2025",
13 address = "Suzhou, China",
14 publisher = "Association for Computational Linguistics",
15 url = "https://aclanthology.org/2025.findings-emnlp.107/",
16 doi = "10.18653/v1/2025.findings-emnlp.107",
17 pages = "2025--2048",
18 ISBN = "979-8-89176-335-7",
19 abstract = "While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date. In this work, we introduce \textbf{EmoBench-UA}, the first annotated dataset for emotion detection in Ukrainian texts. Our annotation schema is adapted from the previous English-centric works on emotion detection (Mohammad et al., 2018; Mohammad, 2022) guidelines. The dataset was created through crowdsourcing using the Toloka.ai platform ensuring high-quality of the annotation process. Then, we evaluate a range of approaches on the collected dataset, starting from linguistic-based baselines, synthetic data translated from English, to large language models (LLMs). Our findings highlight the challenges of emotion classification in non-mainstream languages like Ukrainian and emphasize the need for further development of Ukrainian-specific models and training resources."
20}1@misc{speedbrain_persona_forge,
2 title = {speedbrain injection-engine: Persona Forge emotion models (ONNX)},
3 author = {speed-brain-ai},
4 year = {2026},
5 url = {https://huggingface.co/speed-brain-ai/speedbrain-emotion-uk-onnx}
6}