README.md

---
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- transformers
---

# CoT-MAE MS-Marco Passage Reranker 

CoT-MAE is a transformers based Mask Auto-Encoder pretraining architecture designed for Dense Passage Retrieval.
**CoT-MAE MS-Marco Passage Reranker** is a reranker trained with CoT-MAE retriever mined MS-Marco hard negatives using [Tevatron](github.com/texttron/tevatron) toolkit.

Details can be found in our paper and codes.

Paper: [ConTextual Mask Auto-Encoder for Dense Passage Retrieval](https://arxiv.org/abs/2208.07670).

Code: [caskcsg/ir/cotmae](https://github.com/caskcsg/ir/tree/main/cotmae)

## Scores
### MS-Marco Passage full-ranking + top-200 rerank
We first retrieve using **CoT-MAE MS-Marco Passage Retriever** (named cotmae_base_msmarco_retriever), then use reranker to re-score top-200 retrieval results. Performances are as follows.

| MRR @10 | recall@1 | recall@50 | recall@200 | QueriesRanked  |
|---------|----------|-----------|------------|----------------|
| 0.43884 | 0.304871 | 0.903582  | 0.956734   | 6980           |

## Citations
If you find our work useful, please cite our paper.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2208.07670,
  doi = {10.48550/ARXIV.2208.07670},
  url = {https://arxiv.org/abs/2208.07670},
  author = {Wu, Xing and Ma, Guangyuan and Lin, Meng and Lin, Zijia and Wang, Zhongyuan and Hu, Songlin},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {ConTextual Mask Auto-Encoder for Dense Passage Retrieval},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```