Release snapshot

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README.md

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+---
+license: apache-2.0
+pipeline_tag: image-text-to-text
+language: [multilingual]
+tags:
+  - page-stream-segmentation
+  - document-boundary-detection
+  - document-ai
+  - document-splitting
+  - open-weights
+datasets:
+  - nutrientdocs/doc-split-benchmark
+---
+
+# doc-split-v1 — open-weight
+
+**Where does one document end and the next begin?** An open-weight page-stream-segmentation model you can
+download and run: it splits a stream of pages (a scanned batch / merged PDF) back into its constituent
+documents.
+
+The lightweight, open sibling of the commercial flagship
+[`doc-split-v2`](https://huggingface.co/nutrientdocs/doc-split-v2) — compact, ~4.5× faster, near-flagship
+accuracy on our data and multilingual out of the box. Shipped as **ONNX** — runs with `onnxruntime`, no
+framework or modelling code to install.
+
+- 🎯 **Try it:** [doc-split-demo](https://huggingface.co/spaces/nutrientdocs/doc-split-demo?model=v1)
+- 🏆 **Leaderboard:** [doc-split-leaderboard](https://huggingface.co/spaces/nutrientdocs/doc-split-leaderboard)
+- 📊 **Benchmark:** [doc-split-benchmark](https://huggingface.co/datasets/nutrientdocs/doc-split-benchmark)
+- 🔒 **Higher accuracy?** [doc-split-v2](https://huggingface.co/nutrientdocs/doc-split-v2) (commercial)
+
+## Results — boundary F1 (κ)
+
+Per-page boundary detection, page 0 forced. **This model** vs the private doc-split-v2, the strongest cloud VLM,
+and prior work.
+
+| Cut | **doc-split-v1** | doc-split-v2 | best cloud VLM | OpenPSS specialist |
+|---|---|---|---|---|
+| OpenPSS-short (sparse) | **0.585** (.53) | 0.619 | 0.598 (gemini-flash) | 0.76 |
+| OpenPSS-long | **0.859** (.82) | 0.886 | 0.244 (gemini-flash) | 0.83 |
+| our-200 (synthetic) | **0.936** (.78) | 0.934 | 0.942 (gpt-sol) | — |
+| TABME++ test | **0.704** (.56) | 0.901 | — | — |
+| Tobacco800 test | **0.820** (.60) | 0.957 | — | — |
+| val (real-doc) | **0.918** (.86) | 0.908 | — | — |
+
+Beats every evaluated cloud VLM on OpenPSS-**long** (0.859 vs 0.244) at a fraction of the cost, and holds up
+on our data. TABME++/Tobacco800 are zero-shot for this model (in-domain for doc-split-v2).
+
+## What's in this repo
+
+Runs entirely under `onnxruntime` — nothing else to install.
+
+- `image_model.onnx`, `text_model.onnx` — the image and text towers (per-page embeddings).
+- `head.onnx` — the boundary head (per-page boundary score).
+- `crf.json` — smoothing parameters for the per-page confidence.
+- `tokenizer.json` (+ config) — the bundled text tokenizer.
+
+## Usage (ONNX)
+
+```python
+# pip install onnxruntime transformers numpy huggingface_hub
+import numpy as np, onnxruntime as ort, json
+from transformers import AutoTokenizer
+from huggingface_hub import snapshot_download
+
+d = snapshot_download("nutrientdocs/doc-split-v1")
+img  = ort.InferenceSession(f"{d}/image_model.onnx", providers=["CPUExecutionProvider"])
+text = ort.InferenceSession(f"{d}/text_model.onnx",  providers=["CPUExecutionProvider"])
+head = ort.InferenceSession(f"{d}/head.onnx",        providers=["CPUExecutionProvider"])
+tok  = AutoTokenizer.from_pretrained(d); crf = json.load(open(f"{d}/crf.json"))
+
+def _lse(x, ax):
+    m = x.max(ax, keepdims=True); return (m + np.log(np.exp(x - m).sum(ax, keepdims=True))).squeeze(ax)
+
+def marginals(bl, crf):                    # per-page confidence via forward-backward over a 2-tag chain
+    T = np.asarray(crf["trans"]); s = np.asarray(crf["start"]); e_ = np.asarray(crf["end"])
+    N = len(bl); e = np.stack([np.zeros(N), bl], 1); a = np.zeros((N, 2)); a[0] = s + e[0]
+    for t in range(1, N): a[t] = _lse(a[t-1][:, None] + T, 0) + e[t]
+    b = np.zeros((N, 2)); b[N-1] = e_
+    for t in range(N-2, -1, -1): b[t] = _lse(T + (e[t+1] + b[t+1])[None, :], 1)
+    m = a + b; m -= m.max(1, keepdims=True); p = np.exp(m); return (p / p.sum(1, keepdims=True))[:, 1]
+
+def split(pages, tau=0.5):                 # pages: list of (PIL image, ocr_text or "")
+    arr = np.stack([(np.asarray(im.convert("RGB").resize((512, 512)), np.float32)/255 - .5)/.5
+                    for im, _ in pages]).transpose(0, 3, 1, 2).astype(np.float32)
+    vi = img.run(["image_embed"], {"pixel_values": arr})[0]
+    b = tok(["query: "+(t or " ") for _, t in pages], padding=True, truncation=True,
+            max_length=512, return_tensors="np")
+    vt = text.run(["text_embed"], {"input_ids": b["input_ids"].astype(np.int64),
+                                   "attention_mask": b["attention_mask"].astype(np.int64)})[0]
+    g = np.array([1. if (t and t.strip()) else 0. for _, t in pages], np.float32); N = len(pages)
+    vt = vt * g[:, None]                    # OCR gate: text ignored on pages with no text layer
+    bl = head.run(["boundary_logit"], {"v_img": vi[None], "v_txt": vt[None],
+                   "gate": g[None], "mask": np.ones((1, N), np.float32)})[0][0]
+    bl[0] = 30.0                           # force page 0 to start a document
+    conf = marginals(bl, crf)              # per-page confidence in [0,1]
+    return [1 if (i == 0 or conf[i] >= tau) else 0 for i in range(N)]   # 1 = this page starts a new document
+```
+
+## Intended use & limits
+
+**Use it for:** splitting merged/batch-scanned PDFs into documents; routing; pre-processing for
+classification/extraction. **Limits:** boundary detection only (does not classify document *type*); the
+sparse low-boundary regime (OpenPSS-short) is hardest; OCR text helps on text-heavy pages.
+
+## License
+
+Apache-2.0.
+
+## Calibrated confidence
+
+The raw boundary score is over-confident (a raw 0.85 is really ~63% likely a true boundary). We ship a
+**beta calibration** (fit on held-out data) so the reported confidence is honest and usable as a threshold:
+
+```
+p_calibrated = sigmoid(a·ln(p) + b·ln(1-p) + c),  (a, b, c) = (0.516, -0.402, -0.155)
+```
+ECE 0.044 → 0.012. The demo applies this and lets you set a minimum-confidence threshold on the calibrated value.
+
+## 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.

crf.json

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+{
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+    [
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+    ],
+    [
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+      0.0578848272562027
+    ]
+  ],
+  "start": [
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+    -0.041104089468717575
+  ],
+  "end": [
+    -0.0374150313436985,
+    0.037414439022541046
+  ],
+  "d_img": 768,
+  "d_txt": 384
+}

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special_tokens_map.json

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+    "content": "<s>",
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+    "single_word": false
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+  "mask_token": {
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}