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. 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
- 🏆 Leaderboard: document-classification-leaderboard
- 📊 Benchmark: document-classification-benchmark
- 🏵️ Flagship (commercial): 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. v1 is the
free, open-weight sibling: it trails the commercial 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)
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.
About the author
This project is maintained and funded by Nutrient - 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.