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dpr_bert-base_msmarco_qry-psg-encoder

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bert
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@@ -11,23 +11,23 @@ This model is DPR trained on MS MARCO. The training details and evaluation resul
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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|0.234|
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- |FiQA|0.206|
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- |ArguAna|0.394|
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- |Touché-2020|0.223|
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- |Quora|0.780|
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- |SCIDOCS|0.119|
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- |SciFact|0.494|
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- |NQ|0.439|
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- |HotpotQA|0.453|
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- |Signal-1M|0.202|
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- |TREC-NEWS|0.318|
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- |DBPedia-entity|0.287|
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- |Fever|0.650|
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- |Climate-Fever|0.149|
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- |BioASQ|0.241|
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- |Robust04|0.323|
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- |CQADupStack|0.283|
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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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  |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.