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@@ -14,21 +14,36 @@ This model is presented in the paper [Set-Encoder: Permutation-Invariant Inter-P
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  Code: https://github.com/webis-de/set-encoder
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- We provide the following pre-trained models:
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- | Model Name | TREC DL 19 (BM25) | TREC DL 20 (BM25) | TREC DL 19 (ColBERTv2) | TREC DL 20 (ColBERTv2) |
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- | ------------------------------------------------------------------- | ----------------- | ----------------- | ---------------------- | ---------------------- |
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- | [set-encoder-base](https://huggingface.co/webis/set-encoder-base) | 0.724 | 0.710 | 0.788 | 0.777 |
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- | [set-encoder-large](https://huggingface.co/webis/set-encoder-large) | 0.727 | 0.735 | 0.789 | 0.790 |
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-
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- ## Inference
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-
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- 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:
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  ```bash
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- lightning-ir re_rank --config configs/re-rank.yaml --config configs/set-encoder-finetuned.yaml --config configs/trec-dl.yaml
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  ```
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- ## Fine-Tuning
 
 
 
 
 
 
 
 
 
 
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- WIP
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Code: https://github.com/webis-de/set-encoder
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+ We provide the following pre-trained models for general-purpose re-ranking.
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+ 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:
 
 
 
 
 
 
 
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  ```bash
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+ lightning-ir re_rank --config ./configs/re-rank.yaml --model.model_name_or_path <MODEL_NAME>
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  ```
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+ (nDCG@10 on TREC DL 19 and TREC DL 20)
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+
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+ | Model Name | TREC DL 19 (BM25) | TREC DL 20 (BM25) | TREC DL 19 (ColBERTv2) | TREC DL 20 (ColBERTv2) |
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+ | ---------------------------------------------------------------------------------------- | ----------------- | ----------------- | ---------------------- | ---------------------- |
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+ | [webis/set-encoder-base](https://huggingface.co/webis/set-encoder-base) | 0.746 | 0.704 | 0.781 | 0.768 |
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+ | [webis/set-encoder-large](https://huggingface.co/webis/set-encoder-large) | 0.750 | 0.722 | 0.789 | 0.791 |
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+
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+
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+ ## Citation
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+
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+ If you use this code or the models in your research, please cite our paper:
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+ ```bibtex
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+ @InProceedings{schlatt:2025,
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+ address = {Berlin Heidelberg New York},
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+ 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},
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+ booktitle = {Advances in Information Retrieval. 47th European Conference on IR Research (ECIR 2025)},
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+ doi = {10.1007/978-3-031-88711-6_1},
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+ month = apr,
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+ publisher = {Springer},
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+ series = {Lecture Notes in Computer Science},
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+ site = {Lucca, Italy},
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+ title = {{Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders}},
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+ year = 2025
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+ }