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--- |
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license: mit |
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--- |
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This model is DPR trained on MS MARCO. The training details and evaluation results are as follows: |
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|Model|Pretrain Model|Train w/ Marco Title|Marco Dev MRR@10|BEIR Avg NDCG@10| |
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|:----|:----|:----|:----|:----| |
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|DPR|bert-base-uncased|w/|32.4|35.5| |
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|BERI Dataset|NDCG@10| |
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|:----|:----| |
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|TREC-COVID|58.8| |
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|NFCorpus|23.4| |
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|FiQA|20.6| |
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|ArguAna|39.4| |
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|Touché-2020|22.3| |
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|Quora|78.0| |
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|SCIDOCS|11.9| |
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|SciFact|49.4| |
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|NQ|43.9| |
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|HotpotQA|45.3| |
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|Signal-1M|20.2| |
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|TREC-NEWS|31.8| |
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|DBPedia-entity|28.7| |
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|Fever|65.0| |
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|Climate-Fever|14.9| |
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|BioASQ|24.1| |
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|Robust04|32.3| |
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|CQADupStack|28.3| |
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The implementation is the same as our EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele. |
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``` |
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@inproceedings{sun2022ancetele, |
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title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives}, |
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author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao}, |
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booktitle={Proceedings of EMNLP 2022}, |
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year={2022} |
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} |
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``` |
