Querit/Querit-4B

Querit-Reranker-4B

HighLights

Querit-Reranker-4B is a cross-encoder reranking model developed by the Querit family for multilingual text ranking tasks. The model is initialized from Qwen3-Embedding-4B and further adapted for reranking through task-oriented post-training. By jointly encoding each query-document pair, Querit-Reranker-4B captures fine-grained relevance signals and produces accurate ranking scores for second-stage retrieval. With multilingual supervision, teacher-score distillation, and task-specific checkpoint merging, the model achieves strong performance across multilingual, English, and Chinese reranking benchmarks.

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: 4.02B
• Number of Paramaters (Non-Embedding): 3.63B
• Number of Layers: 36
• Number of Attention Heads: 32
• 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}, 
}