README.md

---
license: apache-2.0
pipeline_tag: zero-shot-image-classification
language:
  - en
tags:
  - zero-shot-image-classification
  - image-classification
  - document-ai
  - open-vocabulary
  - open-weights
datasets:
  - nutrientdocs/document-classification-benchmark
---

# document-classification-v1 — open-weight

**An open-weight, open-vocabulary document classifier you can download and run.** Supply any set of text
labels at inference; the model scores a document image against them by calibrated cosine and returns a
per-label match probability. No fixed class list, no per-class training.

The **open-weight** sibling of the commercial flagship
[`document-classification-v2`](https://huggingface.co/nutrientdocs/document-classification-v2). It ships as
two self-contained **ONNX** graphs — an image tower and a text tower — that you run with `onnxruntime`.
`embed_dim: 1024`; classification `p = sigmoid(scale·cos + bias)` (calibration in
`modules/omni-image/config.json`).

- 🎯 **Try it:** [document-classification-demo](https://huggingface.co/spaces/nutrientdocs/document-classification-demo)
- 🏆 **Leaderboard:** [document-classification-leaderboard](https://huggingface.co/spaces/nutrientdocs/document-classification-leaderboard)
- 📊 **Benchmark:** [document-classification-benchmark](https://huggingface.co/datasets/nutrientdocs/document-classification-benchmark)
- 🏵️ **Flagship (commercial):** [document-classification-v2](https://huggingface.co/nutrientdocs/document-classification-v2)

## Results (macro-F1, zero-shot)

| Benchmark | **v1 (open)** | v2 (commercial) | best cloud VLM |
| --- | ---: | ---: | ---: |
| DocLayNet | 0.75 | **0.97** | 0.83 |
| Forms | 0.80 | **1.00** | 1.00 |
| Tobacco | 0.61 | 0.74 | **0.85** |
| OOD (unseen types) | 0.86 | **0.95** | — |
| OOV (synonym wording) | 0.73 | **0.83** | — |

Every entry is scored by the same open scorer — full ranking, plus a **generalist zero-shot baseline** and
each cloud model, on the
[leaderboard](https://huggingface.co/spaces/nutrientdocs/document-classification-leaderboard). v1 is the
free, open-weight sibling: it trails the commercial [`v2`](https://huggingface.co/nutrientdocs/document-classification-v2)
and the large cloud VLMs on accuracy, but it's Apache-2.0 and downloadable. Like all embedding models it
trails VLMs most on Tobacco (a read-the-header task). ~**5.7 pages/s on an A40** (fused image+text).

## Usage (ONNX)

```python
import numpy as np, onnxruntime as ort, json
from transformers import AutoImageProcessor, AutoTokenizer
from huggingface_hub import hf_hub_download
from PIL import Image

R = "nutrientdocs/document-classification-v1"
img_sess = ort.InferenceSession(hf_hub_download(R, "modules/omni-image/image_model.onnx"))   # SigLIP image tower
txt_sess = ort.InferenceSession(hf_hub_download(R, "modules/omni-image/text_model.onnx"))     # Qwen text tower
cal = json.load(open(hf_hub_download(R, "modules/omni-image/config.json")))["calibration"]
proc = AutoImageProcessor.from_pretrained(R, subfolder="modules/omni-image")   # SigLIP image processor
tok  = AutoTokenizer.from_pretrained(R, subfolder="modules/omni-image")         # Qwen tokenizer

labels = ["invoice", "letter", "memo", "form", "scientific article", "resume"]
calib = lambda cos: 1 / (1 + np.exp(-(cal["scale"] * cos + cal["bias"])))

def embed_text(texts, maxlen):
    e = tok(texts, padding=True, truncation=True, max_length=maxlen, return_tensors="np")
    return txt_sess.run(["text_emb"], {"input_ids": e["input_ids"].astype(np.int64),
                                       "attention_mask": e["attention_mask"].astype(np.int64)})[0]  # [.,1024] L2

lab = embed_text(labels, 64)                                              # label embeds, once

# --- image branch: page image vs labels (image ONNX has batch=1; loop+pool for multi-page) ---
pix = proc(images=[Image.open("doc.png").convert("RGB")], return_tensors="np")["pixel_values"].astype(np.float16)
ie  = img_sess.run(["image_emb"], {"pixel_values": pix})[0]              # [1,1024] L2
image_probs = calib((ie @ lab.T)[0])                                     # [N]

# --- text branch: the page's OCR text vs labels (up to ~2048 tokens) ---
doc_text = open("doc.txt").read()
text_probs = calib((embed_text([doc_text], 2048) @ lab.T)[0])            # [N]

# --- reliability fusion: weight each branch by how DECISIVE it is (top1-top2 margin) ---
margin = lambda p: float(np.partition(p, -2)[-1] - np.partition(p, -2)[-2])
wi, wt = margin(image_probs), margin(text_probs); s = wi + wt + 1e-9
fused = (wi / s) * image_probs + (wt / s) * text_probs
print(dict(zip(labels, fused.round(3).tolist())))
```

## What's in this repo
- `modules/omni-image/{image_model.onnx, text_model.onnx}` — the image + text towers (fp16, `onnxruntime`).
- `modules/omni-image/{config.json, preprocessor_config.json, tokenizer.json}` — calibration + the
  preprocessor and tokenizer needed to run them. That's it — nothing else required.

Open weights under **Apache-2.0** — free to download and run. For the higher-accuracy commercial flagship
(on-prem, calibrated), see [`document-classification-v2`](https://huggingface.co/nutrientdocs/document-classification-v2).

## 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.