license:apache-2.0 region:us
---
license: apache-2.0
---
This is one of the trained models of the AAAI2024 paper: Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization;

You can directly use this model to produce the same results in our main experiments. You may refer to [our github](https://github.com/wangskyGit/passage-sieve/blob/AR2/AR2%2Bpassage_sive/AR2/wiki/co_training_wiki_generate.py) for how to use this model.

Evaluation on NQ testset:

|                        | R@5  | R@20 | R@100 |
| ---------------------- | ---- | ---- | ----- |
| AR2                    | 77.9 | 86.0 | 90.1  |
| AR2+SimANS             | 78.6 | 86.2 | 90.3  |
| AR2+sieve (this model) | 79.2 | 86.4 | 90.7  |

