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Running on Zero
Running on Zero
| 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. | |