This model is DPR 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
|
|
DPR
|
bert-base-uncased
|
w/
|
32.4
|
35.5
|
|
BERI Dataset
|
NDCG@10
|
|
TREC-COVID
|
58.8
|
|
NFCorpus
|
23.4
|
|
FiQA
|
20.6
|
|
ArguAna
|
39.4
|
|
Touché-2020
|
22.3
|
|
Quora
|
78.0
|
|
SCIDOCS
|
11.9
|
|
SciFact
|
49.4
|
|
NQ
|
43.9
|
|
HotpotQA
|
45.3
|
|
Signal-1M
|
20.2
|
|
TREC-NEWS
|
31.8
|
|
DBPedia-entity
|
28.7
|
|
Fever
|
65.0
|
|
Climate-Fever
|
14.9
|
|
BioASQ
|
24.1
|
|
Robust04
|
32.3
|
|
CQADupStack
|
28.3
|
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}
}