
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"](https://arxiv.org/pdf/2210.17167.pdf). 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}
}
```
{"license": "mit"}
transformers pytorch bert feature-extraction arxiv:2210.17167 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2210.17167 #license-mit #endpoints_compatible #region-us 

This model is DPR trained on MS MARCO. The training details and evaluation results are as follows:




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 URL



TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2210.17167 #license-mit #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2210.17167 #license-mit #endpoints_compatible #region-us 
