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@@ -104,5 +104,15 @@ After training the model, you can evaluate the model by running the following co
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  python src/convert2trec.py output/res.step-20 && python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.step-20.trec && path_to/trec_eval -m ndcg_cut.5 data/qrels.dev.tsv res.step-20.trec
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  ```
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  python src/convert2trec.py output/res.step-20 && python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.step-20.trec && path_to/trec_eval -m ndcg_cut.5 data/qrels.dev.tsv res.step-20.trec
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  ```
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+ ## Citation
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+ If you use this dataset in your research, please cite our paper:
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+ ```
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+ @inproceedings{t2ranking,
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+ title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
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+ author={Xiaohui Xie, Qian Dong},
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+ booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
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+ year={2023}
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+ }
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+ ```
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