README.md

---
license: apache-2.0
pipeline_tag: feature-extraction
library_name: transformers
---

# Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders

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.

Code: https://github.com/webis-de/set-encoder

We provide the following pre-trained models:

| 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                  |

## Inference

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:

```bash
lightning-ir re_rank --config configs/re-rank.yaml --config configs/set-encoder-finetuned.yaml --config configs/trec-dl.yaml
```

## Fine-Tuning

WIP