readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # CoT-MAE MS-Marco Passage Retriever
  
  CoT-MAE is a transformers based Mask Auto-Encoder pretraining architecture designed for Dense Passage Retrieval.
  **CoT-MAE MS-Marco Passage Retriever** is a retriever trained with BM25 hard negatives and CoT-MAE retriever mined MS-Marco hard negatives using [Tevatron](github.com/texttron/tevatron) toolkit. Specifically, we trained a stage-one retriever using BM25 HN, using stage-one retriever to mine HN, then trained a stage-two retriever using both BM25 HN & stage-one retriever mined hn. The release is the stage-two retriever.
  
  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
  
  | MRR @10  | recall@1 | recall@50 | recall@1k | QueriesRanked  |
  |----------|----------|-----------|-----------|----------------|
  | 0.394431 | 0.265903 | 0.870344  | 0.986676  | 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}
  }
  ```
  