This model is ANCE-Tele trained on MS MARCO. The training details and evaluation results are as follows:
Model | Pretrain Model | Train w/ Marco Title | Marco Dev MRR@10 | BEIR Avg NDCG@10 |
---|---|---|---|---|
ANCE-Tele | cocodr-base | w/o | 37.3 | 44.2 |
BERI Dataset | NDCG@10 |
---|---|
TREC-COVID | 77.4 |
NFCorpus | 34.4 |
FiQA | 29.0 |
ArguAna | 45.6 |
Touché-2020 | 22.3 |
Quora | 85.8 |
SCIDOCS | 14.6 |
SciFact | 71.0 |
NQ | 50.5 |
HotpotQA | 58.8 |
Signal-1M | 27.2 |
TREC-NEWS | 34.7 |
DBPedia-entity | 36.2 |
Fever | 71.4 |
Climate-Fever | 17.9 |
BioASQ | 42.1 |
Robust04 | 41.4 |
CQADupStack | 34.9 |
The implementation is the same as our EMNLP 2022 paper "Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives". The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}
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