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BERT Reranker for MS-MARCO Document Ranking
Model description
A text reranker trained for BM25 retriever on MS MARCO document dataset.
Intended uses & limitations
It is possible to work with other retrievers like but using aligned BM25 works the best.
We used anserini toolkit's BM25 implementation and indexed with tuned parameters (k1=3.8, b=0.87) following this instruction.
How to use
See our project repo page.
Eval results
MRR @10: 0.423 on Dev.
BibTeX entry and citation info
@inproceedings{gao2021lce,
title={Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline},
author={Luyu Gao and Zhuyun Dai and Jamie Callan},
year={2021},
booktitle={The 43rd European Conference On Information Retrieval (ECIR)},
}
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