Create README.md

README.md

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