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/co_training/co_training_marco_generate.py) for how to use this model.

Evaluation on MS development set:

|                        | MRR@5  | R@50 | R@1k |
| ---------------------- | ---- | ---- | ----- |
| AR2                    | 39.5 | 87.8 | 98.6  |
| AR2+sieve (this model) | 39.8 | 88.3 | 98.6  |

