readme: ---
  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}
  }
  ```
  