caskcsg/cotmae_base_msmarco_reranker

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 toolkit.

Details can be found in our paper and codes.

Paper: ConTextual Mask Auto-Encoder for Dense Passage Retrieval.

Code: caskcsg/ir/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.

@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}
}