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ThinMQM-7B
rzzhan/ThinMQM-7B
ThinMQM-7B is a machine learning model from rzzhan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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At a glance
- License
- mit
- Model type
- qwen2
- Access
- Public
- Created
- Sep 27, 2025
- Updated
- Oct 24, 2025
- SHA
- 8a7b65ed
Datasets
Model card
From the Hugging Face model README
<div align="center">
<h1 style="display: flex; justify-content: center; align-items: center; gap: 10px; margin: 0;">
Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost
</h1>
Metrics
| Metric/Model | Avg. | En-De SPA (%) | En-De $Acc^*_eq$ | En-Es SPA (%) | En-Es $Acc^*_eq$ | Ja-Zh SPA (%) | Ja-Zh $Acc^*_eq$ |
|---|---|---|---|---|---|---|---|
| QwQ 32B | 68.3 | 79.8 | 46.8 | 76.1 | 68.0 | 91.9 | 46.9 |
| + ThinMQM | 72.2 (+3.9) | 83.2 (+3.4) | 52.5 (+5.7) | 80.7 (+4.6) | 69.2 (+1.2) | 91.3 (−0.6) | 56.1 (+9.2) |
| R1-Distill-Llama-8B | 64.9 | 71.8 | 42.9 | 78.5 | 68.0 | 84.7 | 43.5 |
| + ThinMQM | 70.8 (+5.9) | 85.5 (+13.7) | 48.6 (+5.7) | 81.3 (+2.8) | 68.2 (+0.2) | 90.5 (+5.8) | 51.0 (+7.5) |
| R1-Distill-Qwen-7B | 61.1 | 67.3 | 42.9 | 61.0 | 68.0 | 83.8 | 43.5 |
| + ThinMQM | 69.8 (+8.7) | 84.5 (+17.2) | 48.5 (+5.6) | 77.8 (+16.8) | 68.0 (+0.0) | 89.0 (+5.2) | 51.3 (+7.8) |
Model & Data Card
| Released Models | HF Model | Template | Trained Dataset |
|---|---|---|---|
| rzzhan/ThinMQM-32B | https://huggingface.co/rzzhan/ThinMQM-32B | thinking | https://huggingface.co/datasets/rzzhan/ThinMQM-12k/ thinmqm12k_src |
| rzzhan/ThinMQM-8B | https://huggingface.co/rzzhan/ThinMQM-32B | thinking_ref | https://huggingface.co/datasets/rzzhan/ThinMQM-12k/ thinmqm12k_ref |
| rzzhan/ThinMQM-7B | https://huggingface.co/rzzhan/ThinMQM-32B | thinking_ref | https://huggingface.co/datasets/rzzhan/ThinMQM-12k/ thinmqm12k_ref |
📝 Citation
If you find our model, data, or evaluation code useful, please kindly cite our paper:
@article{zhan2025thinmqm,
title={Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost},
author={Zhan, Runzhe and Huang, Zhihong and Yang, Xinyi and Chao, Lidia S and Yang, Min and Wong, Derek F},
year={2025},
journal = {ArXiv preprint},
volume = {2510.20780},
url={https://arxiv.org/abs/2510.20780},
}
📬 Contact
For questions, feedback, or collaboration opportunities, feel free to reach out:
- Runzhe Zhan: [email protected]
📄 License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
- Type
- qwen2
- License
- mit
- Created
- Sep 27, 2025
- Updated
- Oct 24, 2025