---
title: InfoMax Reranker
emoji: 🔎
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 6.31.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Entropy-minimising passage reranker vs MS MARCO CE
---

# InfoMax Reranker

A greedy, information-maximising selector over a retrieved candidate pool, compared with the standard
`cross-encoder/ms-marco-MiniLM-L6-v2` reranker. Neural scoring runs on ZeroGPU; selection runs on CPU.

- `app.py`: Gradio app. Paste a query and passages separated by blank lines.
- `infomax_rerank.py`: the selectors: `infomax`, plus the baselines `mmr`, `fused`/`zfuse` and `by_score`.
- `METHOD.md`: the model and the selection rule.
- `results/`: BEIR test results (`beir_results.md`, `beir_results.json`).
- `benchmark/`: the scripts that produced them (see below).

## Hardware

Create the Space with the Gradio SDK and select **ZeroGPU** as hardware. Hosting a ZeroGPU Space needs a PRO or Enterprise
account. The `@spaces.GPU` decorator has no effect elsewhere, so the app also runs on CPU hardware and locally:

```
pip install -r requirements.txt gradio
python app.py
```

## Reproducing the BEIR numbers

The datasets are the standard BEIR zips (scifact, nfcorpus, fiqa, scidocs, trec-covid), unzipped into `$BEIR_DATA`.
Outputs go to `$EXP_OUT`. Models are loaded from local folders or Hub ids.

```
cd benchmark
export BEIR_DATA=/path/to/beir EXP_OUT=/path/to/out
python bm25_pools.py                                   # BM25 top-100 pools: test splits + 200-query dev samples
python dense_stage.py sentence-transformers/all-MiniLM-L6-v2 50
python ce_stage.py cross-encoder/ms-marco-MiniLM-L6-v2 50
python build_scores.py 50
python run_eval.py            # tune every method on dev splits only -> best_params.json
python run_eval.py --test     # nDCG / Recall on the test splits + paired bootstrap CIs
python mrr_recall.py          # MRR@10, Recall@3/5/10
```

As a check on the setup, BM25 nDCG@10 is within about 0.01 of the published BEIR BM25 numbers on all five datasets.
all-MiniLM-L6-v2 scores 0.648 nDCG@10 on SciFact over the full corpus.