Update README.md

README.md

@@ -8,29 +8,42 @@ tags:
 - cross-encoder
 ---
 
-# Set-Encoder
+# Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
 
-This repository contains the code for the paper: [`Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders`](https://arxiv.org/abs/2404.06912).
+This model is presented in the paper [Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders](https://huggingface.co/papers/2404.06912). It's a cross-encoder architecture designed for efficient and permutation-invariant passage re-ranking.
 
-We use [`lightning-ir`](https://github.com/webis-de/lightning-ir) to train and fine-tune models. Download and install the library to use the code in this repository.
+Code: https://github.com/webis-de/set-encoder
 
-## Model Zoo
+We provide the following pre-trained models for general-purpose re-ranking.
 
-We provide the following pre-trained models:
+To reproduce the results, run the following command using the [Lightning IR](https://github.com/webis-de/lightning-ir) library and the configuration files from the repository repository linked above:
 
-| Model Name                                                          | TREC DL 19 (BM25) | TREC DL 20 (BM25) | TREC DL 19 (ColBERTv2) | TREC DL 20 (ColBERTv2) |
-| ------------------------------------------------------------------- | ----------------- | ----------------- | ---------------------- | ---------------------- |
-| [set-encoder-base](https://huggingface.co/webis/set-encoder-base)   | 0.724             | 0.710             | 0.788                  | 0.777                  |
-| [set-encoder-large](https://huggingface.co/webis/set-encoder-large) | 0.727             | 0.735             | 0.789                  | 0.790                  |
+```bash
+lightning-ir re_rank --config ./configs/re-rank.yaml --model.model_name_or_path <MODEL_NAME>
+```
 
-## Inference
+(nDCG@10 on TREC DL 19 and TREC DL 20)
 
-We recommend using the `lightning-ir` cli to run inference. The following command can be used to run inference using the `set-encoder-base` model on the TREC DL 19 and TREC DL 20 datasets:
+| Model Name                                                                               | TREC DL 19 (BM25) | TREC DL 20 (BM25) | TREC DL 19 (ColBERTv2) | TREC DL 20 (ColBERTv2) |
+| ---------------------------------------------------------------------------------------- | ----------------- | ----------------- | ---------------------- | ---------------------- |
+| [webis/set-encoder-base](https://huggingface.co/webis/set-encoder-base)                  | 0.746             | 0.704             | 0.781                  | 0.768                  |
+| [webis/set-encoder-large](https://huggingface.co/webis/set-encoder-large)                | 0.750             | 0.722             | 0.789                  | 0.791                  |
 
-```bash
-lightning-ir re_rank --config configs/re-rank.yaml --config configs/set-encoder-finetuned.yaml --config configs/trec-dl.yaml
-```
 
-## Fine-Tuning
+## Citation
+
+If you use this code or the models in your research, please cite our paper:
 
-WIP
+```bibtex
+@InProceedings{schlatt:2025,
+  address =                  {Berlin Heidelberg New York},
+  author =                   {Ferdinand Schlatt and Maik Fr{\"o}be and Harrisen Scells and Shengyao Zhuang and Bevan Koopman and Guido Zuccon and Benno Stein and Martin Potthast and Matthias Hagen},
+  booktitle =                {Advances in Information Retrieval. 47th European Conference on IR Research (ECIR 2025)},
+  doi =                      {10.1007/978-3-031-88711-6_1},
+  month =                    apr,
+  publisher =                {Springer},
+  series =                   {Lecture Notes in Computer Science},
+  site =                     {Lucca, Italy},
+  title =                    {{Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders}},
+  year =                     2025
+}