| | --- |
| | library_name: transformers |
| | tags: [] |
| | --- |
| | |
| | # Model Card for RankMistral |
| |
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| | RankMistral, finetuned from [Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) using [rank_llm dataset](https://huggingface.co/datasets/castorini/rank_llm_data). |
| |
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| | ## Results |
| | From [QPP-RA: Aggregating Large Language Model Rankings](https://dl.acm.org/doi/pdf/10.1145/3731120.3744575) |
| | Using the [Rank LLM Library](https://github.com/castorini/rank_llm). |
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| | ## Citation |
| | If you use this model please cite: |
| |
|
| | ``` |
| | @inproceedings{10.1145/3731120.3744575, |
| | author = {Betello, Filippo and Russo, Matteo and D\"{u}tting, Paul and Leonardi, Stefano and Silvestri, Fabrizio}, |
| | title = {QPP-RA: Aggregating Large Language Model Rankings}, |
| | year = {2025}, |
| | isbn = {9798400718618}, |
| | publisher = {Association for Computing Machinery}, |
| | address = {New York, NY, USA}, |
| | url = {https://doi.org/10.1145/3731120.3744575}, |
| | doi = {10.1145/3731120.3744575}, |
| | booktitle = {Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR)}, |
| | pages = {103–114}, |
| | numpages = {12}, |
| | keywords = {llm, query performance prediction, rank aggregation}, |
| | location = {Padua, Italy}, |
| | series = {ICTIR '25} |
| | } |
| | ``` |
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