Model Card for Panther Read Intent Classifier

Multilingual User-Intent & Request-Routing Classifier for Real-World AI Agent Security

Read more

• 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

Panther Read is a multilingual ModernBERT-based (mmBERT) classifier that detects the operational intent of a request and routes it to the right capability. It is part of the Patronus Protect security stack and is the dedicated single-head counterpart to the routing head of Lion Warden.

Intended Uses

The model maps an input text to exactly one class:

Examples:

Typical downstream uses:

• request routing and capability selection,
• AI agent orchestration,
• policy and approval routing,
• runtime monitoring.

Limitations

• A positive prediction describes an apparent property of the input, not proof that an action was executed.
• The model does not track information flow across multiple agent steps.
• German and English are the primary evaluated languages; other languages run through the multilingual backbone but were not actively validated.
• False positives and negatives are possible. High-impact enforcement should combine the model with deterministic policy and calibrated thresholds.

Model Variants

• Panther Read Intent Classifier: full ModernBERT model in FP32 (model.safetensors).
• Panther Read Intent Classifier ONNX (FP16): onnx/onnx_fp16/model_fp16.onnx in this repository.
• Panther Read Intent Classifier Edge: quantized ONNX builds (int8, int8_int4_embeddings, fp16) in a separate edge repository.
• Panther Read Intent Classifier NTDB L2: lightweight multilingual cascade components under l2/ for efficient local runtime classification.

Training Data

Trained on Patronus' in-house multilingual dataset for this task, built from cleaned real-world sources plus internally generated examples. Real-world sources were judge-cleaned by content (no keyword heuristics) and contaminated rows removed.

Augmentations

To improve robustness the dataset includes modern obfuscation techniques:

• Unicode variants
• Homoglyph attacks
• Encodings (e.g. base64)
• Tag wrappers (User:, System:)
• HTML tags
• Code comments
• Spacing noise
• Leetspeak
• Case noise
• Combination of N augmentation techniques

Regularization

• Natural-language wrappers around the payload
• Counterfactual samples
• Trigger-word / spurious-correlation corpora
• ~90% similarity deduplication with a train/(val ∪ test) leakage guard

Reducing bias

All augmentations and regularizers are applied to positive and negative examples alike so the model keys on content rather than surface form.

Benchmark

Held-out test set (n = 1,880), single-label:

Per-class F1:

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="patronus-studio/panther-read-intent-classifier")
clf("Chart the weekly conversion rate from the signups table")
# -> [{"label": "data_analytics_request", "score": 0.98}]

ONNX

The FP16 ONNX export lives under onnx/onnx_fp16; the quantized builds (int8, int8_int4_embeddings) live in the separate Panther Read Intent Classifier Edge repository. Apply a softmax over the logits and take the argmax:

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer

model_id = "patronus-studio/panther-read-intent-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = ORTModelForSequenceClassification.from_pretrained(model_id, subfolder="onnx/onnx_fp16", file_name="model_fp16.onnx")

inputs = tokenizer("Chart the weekly conversion rate from the signups table", return_tensors="pt")
logits = model(**inputs).logits.detach().cpu().numpy()[0]
print(model.config.id2label[int(logits.argmax())])

Citation

@misc{pantherread2026,
  title={Panther Read Intent Classifier: Multilingual Classification for Real-World AI Agent Security},
  author={Patronus Protect},
  year={2026},
  howpublished={\url{https://huggingface.co/patronus-studio/panther-read-intent-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): 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

🛡️ Patronus Protect

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Try it for free at patronus.studio.