# Submit to the Doc-Split leaderboard

1. **Predict** boundaries for every stream in each cut: `{stream_id: [0/1 per page, in position order]}`
   (1 = page starts a new document; page 0 is a forced boundary). Cuts: `our200` (this repo) and the
   OpenPSS SHORT/LONG cuts from [`openpss-mirror`](https://huggingface.co/datasets/nutrientdocs/openpss-mirror).
2. **Score** per cut: `python score.py --pred preds.json --config our200` → `{f1, kappa, windowdiff, pk, ...}`.
3. **Open a PR** on the [leaderboard Space](https://huggingface.co/spaces/nutrientdocs/doc-split-leaderboard)
   adding `results/<model>-<cut>.json`:

```json
{ "name": "your-model", "model": "org/your-model", "cut": "openpss_short",
  "open": true, "n": 108, "f1": 0.00, "kappa": 0.00,
  "hardware": "A100", "inputs": "image+text" }
```

Report **κ alongside F1** — on the sparse cuts a degenerate predict-none/all scores κ ≈ 0 despite a
misleading F1. Note whether your model saw these domains in training (in-domain vs zero-shot).