| | --- |
| | pipeline_tag: text-ranking |
| | 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} |
| | } |
| | ``` |
| | |
