Add comprehensive model card for LimRank-7B

README.md

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+---
+license: mit
+library_name: transformers
+pipeline_tag: text-ranking
+---
+
+# LimRank: Less is More for Reasoning-Intensive Information Reranking
+
+This repository contains the `limrank-7b` model, based on `Qwen2.5-7B`, which was presented in the paper [LimRank: Less is More for Reasoning-Intensive Information Reranking](https://huggingface.co/papers/2510.23544).
+
+LimRank demonstrates an efficient approach to adapt modern Large Language Models (LLMs) for reasoning-intensive information reranking tasks. This is achieved by leveraging `LIMRANK-SYNTHESIZER`, a reusable and open-source pipeline that generates minimal yet high-quality synthetic supervision data. Through this approach, LimRank achieves competitive performance on challenging benchmarks like BRIGHT and FollowIR, utilizing less than 5% of the data typically required by prior methods. The model also shows strong generalization capabilities across various downstream tasks, including scientific literature search and retrieval-augmented generation.
+
+## Links
+-   **Paper**: [LimRank: Less is More for Reasoning-Intensive Information Reranking](https://huggingface.co/papers/2510.23544)
+-   **Code/GitHub Repository**: [https://github.com/SighingSnow/LimRank](https://github.com/SighingSnow/LimRank)
+-   **Trained LimRank Model**: [songtingyu/limrank-7b](https://huggingface.co/songtingyu/limrank)
+-   **Training Datasets**: [songtingyu/limrank-data](https://huggingface.co/datasets/songtingyu/limrank-data)
+-   **Evaluation Results**: [sogntingyu/limrank-results](https://huggingface.co/datasets/songtingyu/limrank-results)
+-   **Running Files for Reproduction**: [songtingyu/limrank-run-files](https://huggingface.co/datasets/songtingyu/limrank-run-files)
+
+## Citation
+If you find our paper useful, please cite our work:
+
+```bibtex
+@misc{song2025limrankreasoningintensiveinformationreranking,
+      title={LimRank: Less is More for Reasoning-Intensive Information Reranking}, 
+      author={Tingyu Song and Yilun Zhao and Siyue Zhang and Chen Zhao and Arman Cohan},
+      year={2025},
+      eprint={2510.23544},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2510.23544}, 
+}
+```
+
+## Acknowledgements
+We would like to thank the authors of the following papers and repos for their open-source contributions. 
+*   [rank1](https://github.com/orionw/rank1)
+*   [ReasonIR](https://github.com/facebookresearch/ReasonIR) 
+*   [MTEB](https://github.com/embeddings-benchmark/mteb)
+
+## License
+The model is released under the [MIT License](https://github.com/SighingSnow/LimRank/blob/main/LICENSE).