orca-sonar-document-classifier

Model Card for Orca Sonar Document Classifier

Multilingual Document Topic Classifier for Real-World AI Security & DLP

Read more

• Blog post (EN): Orca-Sonar: Our Multilingual Document Classifier for AI Security
• Blogbeitrag (DE): Orca-Sonar: unser mehrsprachiger Dokumenten-Klassifikator für AI-Security
• Blog post (EN): Our AI-Security Model Zoo Is Now Open Source
• Blogbeitrag (DE): Unser AI-Security-Model-Zoo ist jetzt Open Source
• Product page: Patronus AI models

Orca-Sonar is a Multilingual ModernBERT-based (mmBERT) classifier that assigns a document/text to one of 9 topic classes. It is part of the Patronus Protect security stack and is designed for topic-/risk-routing of incoming texts (e.g. before they reach an LLM, a DLP gate, or a storage tier).

It classifies German and English text and is robust to user-to-AI wrappers (e.g. "Summarize this contract: …"), i.e. the topic of the content determines the class, not the surface format of the request.

Intended Uses

The model maps an input text to one of:

Disambiguation: on a tie, the more sensitive class wins: legal > hr > finance > internal_and_tech > source_code > marketing > other.

Limitations

• Highly accurate on German and English; other languages were not actively tested.
• The model can produce false positives; for high-stakes routing combine it with a confidence/abstention gate.
• Robustness against adversarial / out-of-distribution / pure-PII / pathological-length inputs is partial; pair the model with a deterministic pre-gate (length + PII) for production DLP use.

Model Variants

• orca-sonar: full model (model.safetensors, fp32).
• orca-sonar-fp16 (ONNX): FP16 ONNX export under onnx/onnx_fp16/, half the size, argmax-faithful to the full model.

Training Data

Trained on our own in-house dataset (German + English, 9 topic classes), purpose-built for this model. The dataset will be published soon.

Benchmark

Held-out test set (100 % real data), per-class F1:

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="patronus-studio/orca-sonar-document-classifier")
clf("Fasse mir diesen Dienstleistungsvertrag zusammen: Laufzeit 24 Monate, Gerichtsstand München …")
# -> [{'label': 'legal', 'score': 0.99}]

ONNX

An FP16 ONNX version is available under onnx/onnx_fp16/:

import torch
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer

model_id = "patronus-studio/orca-sonar-document-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = ORTModelForSequenceClassification.from_pretrained(model_id, subfolder="onnx/onnx_fp16")

inputs = tokenizer("def add(a, b):\n    return a + b", return_tensors="pt")
logits = model(**inputs).logits
print(model.config.id2label[int(torch.argmax(logits, dim=-1))])

Citation

@misc{orcasonar2026,
  title={Orca-Sonar: Multilingual Document Topic Classification for Real-World AI Security},
  author={Patronus Protect},
  year={2026},
  howpublished={\url{https://huggingface.co/patronus-studio/orca-sonar-document-classifier}}
}

License

This model is released under the Apache License 2.0. A copy of the license is included as LICENSE in this repository.

The model is derived from jhu-clsp/mmBERT-small, which is distributed under the MIT License. The upstream copyright and permission notice are retained; the MIT terms continue to apply to the portions originating from that work.

Patronus Ark

This model is built to run inside Patronus Ark, Patronus' open-source on-device AI-security scanning library (L1 native rules → L2 NTDB cascade → L3 transformer). Ark is open source: GitHub repository · product page.

More information

• Blog post (EN): Orca-Sonar: Our Multilingual Document Classifier for AI Security
• Blogbeitrag (DE): Orca-Sonar: unser mehrsprachiger Dokumenten-Klassifikator für AI-Security
• Blog post (EN): Our AI-Security Model Zoo Is Now Open Source
• Blogbeitrag (DE): Unser AI-Security-Model-Zoo ist jetzt Open Source
• Product page: Patronus AI models
• Patronus Ark, the open-source scanning library this model runs in: GitHub · product page
• Patronus Protect, the on-device AI firewall: patronus.studio

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