Querit-Reranker

HighLights

Querit-Reranker is the first self-developed model released by the Querit family, specifically designed for text ranking tasks. This model is based on Querit’s self-developed MoE foundation model, and inherits the foundation model’s excellent multilingual capabilities, long text comprehension, and reasoning abilities. It has also undergone post-training customization for ranking tasks using a vast amount of open-source and proprietary data, achieving a technological breakthrough in multilingual text ranking tasks.

Model Description

• Model type: Text Reranking
• Language(s) (NLP): Multilingual (Chinese, English, Spanish, French, German, Russian, Korean, Japanese)
• Training Stage: Pretraining & Post-training
• Number of Total Parameters: 4.92B-A0.43B
• Number of Paramaters (Non-Embedding): 4.79B
• Number of Layers: 24
• Number of Attention Heads: 16
• Context Length: 128k

Citation

If you find Querit-Reranker useful for your research or applications, please cite our paper:

Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation Yunfei Zhong, Jun Yang, Wei Huang, Yinqiong Cai, Haosheng Qian, Yixing Fan, Ruqing Zhang, Lixin Su, Daiting Shi, and Jiafeng Guo. arXiv:2606.19037, 2026.

@misc{zhong2026queritrerankertrainingcompactmultilingual,
      title={Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation}, 
      author={Yunfei Zhong and Jun Yang and Wei Huang and Yinqiong Cai and Haosheng Qian and Yixing Fan and Ruqing Zhang and Lixin Su and Daiting Shi and Jiafeng Guo},
      year={2026},
      eprint={2606.19037},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2606.19037}, 
}