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--- |
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language: |
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- en |
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base_model: |
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- google/electra-base-discriminator |
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pipeline_tag: text-ranking |
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--- |
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monoELECTRA is a highly effective cross-encoder reranker built on `google/electra-base-discriminator` and trained on MS MARCO passage data for 300K steps with a batch size of 16. |
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It uses hard negatives from strong first-stage retrievers and the Localized Contrastive Estimation (LCE) loss with large group sizes (up to 31 negatives per positive). |
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This setup consistently outperforms standard monoBERT and hinge/CE baselines, especially in the top-$k$ pool where near-duplicate passages matter. |
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If you want a compact, supervised reranker that was tuned to squeeze every last bit of signal from hard negatives, use this one. |
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If you use the monoELECTRA model, please cite the following relevant paper: |
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[Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking](https://arxiv.org/abs/2312.02724) |
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<!-- {% raw %} --> |
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``` |
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@inproceedings{squeezemonoelectra2022, |
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author = {Pradeep, Ronak and Liu, Yuqi and Zhang, Xinyu and Li, Yilin and Yates, Andrew and Lin, Jimmy}, |
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title = {Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking}, |
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year = {2022}, |
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publisher = {Springer-Verlag}, |
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address = {Berlin, Heidelberg}, |
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booktitle = {Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part I}, |
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pages = {655–670}, |
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numpages = {16}, |
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location = {Stavanger, Norway} |
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} |
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``` |
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