---
license: other
license_name: nutrient-commercial
pipeline_tag: image-text-to-text
language: [multilingual]
tags:
  - page-stream-segmentation
  - document-boundary-detection
  - document-ai
  - document-splitting
datasets:
  - nutrientdocs/doc-split-benchmark
---

# doc-split-v2 · _commercial_

**One model that splits any document stream — short or long, English or not, forms or prose.** The
high-accuracy flagship for page-stream segmentation: hand it a merged/scanned PDF and it marks where each
new document begins. **Weights are commercial** (not downloadable here); this page is a spec + scorecard.

It is the document specialist in a field of generalists — cloud VLMs and OpenPSS's own single-slice
specialists each fall down somewhere; this one does short *and* long streams with one model. For the
open-weight companion, see [doc-split-v1](https://huggingface.co/nutrientdocs/doc-split-v1).

- 🎯 **Try it:** [doc-split-demo](https://huggingface.co/spaces/nutrientdocs/doc-split-demo?model=flagship)
- 🏆 **Leaderboard:** [doc-split-leaderboard](https://huggingface.co/spaces/nutrientdocs/doc-split-leaderboard)
- 📊 **Benchmark:** [doc-split-benchmark](https://huggingface.co/datasets/nutrientdocs/doc-split-benchmark)
- 🟢 **Open weights:** [doc-split-v1](https://huggingface.co/nutrientdocs/doc-split-v1)

## Results — boundary F1 (κ)

**This model** vs the open `doc-split-v1`, the strongest cloud VLM, and prior work (bold = best releasable single model).

| Cut | **doc-split-v2** | doc-split-v1 (open) | best cloud VLM | OpenPSS specialist |
|---|---|---|---|---|
| OpenPSS-short (sparse) | **0.652** (.60) | 0.585 | 0.598 | 0.76 (short-spec) |
| OpenPSS-long | **0.891** (.86) | 0.859 | 0.244 | 0.83 (long-spec) |
| our-200 (synthetic) | **0.944** (.79) | 0.936 | 0.942 | — |
| TABME++ test | **0.943** (.91) | 0.704 | — | — |
| Tobacco800 test | **0.969** (.93) | 0.820 | — | — |
| val (real-doc) | **0.917** (.86) | 0.918 | — | — |

**One balanced model, not two specialists.** OpenPSS needs a separate short- and long-specialist (each
craters on the other slice); the flagship does both with one model, and its OpenPSS-**long** (0.891) tops
even OpenPSS's own long-specialist (0.83) and every cloud VLM (best 0.244). It also dominates the modern
TABME++/Tobacco800 benchmarks. See the [leaderboard](https://huggingface.co/spaces/nutrientdocs/doc-split-leaderboard)
for the full field.

## Intended use & limits

- **Use it for:** segmenting a stream of page images (a merged/scanned PDF) into its constituent documents —
  short or long, English or not, forms or prose. One model handles both sparse and dense boundary regimes;
  the embedded text layer sharpens boundaries when present, and scanned pages fall back to vision.
- **Limits:** optimized for **document** page streams; confidence is calibrated on our-domain data, so very
  out-of-distribution scans are approximate. Boundaries only (not document *type*).

## License & data

The model **weights** are offered under a commercial Nutrient license — deployed on-prem, so your documents
never leave your infrastructure. The training set is not redistributed; evaluation runs on the held-out
[doc-split-benchmark](https://huggingface.co/datasets/nutrientdocs/doc-split-benchmark).

> ### 📩 Get access
>
> `doc-split-v2` is commercial and its weights are not downloadable here. To run it on-prem —
> one model for any stream, calibrated, private — **contact Nutrient:
> [nutrient.io/contact-sales](https://www.nutrient.io/contact-sales/).**

## About the author

<a href="https://nutrient.io/">
  <img src="https://avatars2.githubusercontent.com/u/1527679?v=3&s=200" height="80" />
</a>

This project is maintained and funded by [Nutrient](https://nutrient.io/) - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.