---
library_name: onnx
pipeline_tag: text-classification
license: apache-2.0
language:
  - en
tags:
  - intentguard
  - guardrails
  - llm-safety
  - content-moderation
  - legal
  - deberta-v2
  - onnx-runtime
  - intent-classification
  - chatbot-security
model-index:
  - name: intentguard-legal
    results:
      - task:
          type: text-classification
          name: Intent Classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 97.9
          - name: Legitimate Block Rate
            type: accuracy
            value: 0.0
          - name: Off-Topic Pass Rate
            type: accuracy
            value: 0.5
---

# IntentGuard — Legal & Compliance

[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Accuracy](https://img.shields.io/badge/accuracy-97.9%25-brightgreen.svg)](#performance) [![Size](https://img.shields.io/badge/model_size-2.5MB-orange.svg)](#model-details) [![Latency](https://img.shields.io/badge/p99_latency-<30ms_CPU-green.svg)](#performance) [![Format](https://img.shields.io/badge/format-ONNX_INT8-purple.svg)](#model-details)

**Production-ready vertical intent classifier for LLM chatbot guardrails. Classifies user messages as `allow`, `deny`, or `abstain` to keep legal chatbots on-topic and within professional boundaries.**

[Research Article](https://perfecxion.ai/articles/intentguard-vertical-intent-classifier-llm-guardrails.html) | [perfecXion.ai](https://perfecxion.ai) | [Finance Model](https://huggingface.co/perfecXion/intentguard-finance) | [Healthcare Model](https://huggingface.co/perfecXion/intentguard-healthcare) | [Legal Model](https://huggingface.co/perfecXion/intentguard-legal)

---

## IntentGuard Model Family

| Model | Vertical | Accuracy | Off-Topic Pass Rate | Link |
|-------|----------|----------|---------------------|------|
| **intentguard-finance** | Financial Services | 99.6% | 0.00% | [perfecXion/intentguard-finance](https://huggingface.co/perfecXion/intentguard-finance) |
| **intentguard-healthcare** | Healthcare & Clinical | 98.9% | 0.98% | [perfecXion/intentguard-healthcare](https://huggingface.co/perfecXion/intentguard-healthcare) |
| **intentguard-legal** | Legal & Compliance | **97.9%** | 0.50% | This model |

---

## Overview

### The Problem

Legal chatbots carry significant liability risk — unauthorized practice of law, confidentiality breaches, and jurisdictional issues demand strict topic boundaries. An LLM that happily answers questions about celebrity gossip or sports while branded as a legal assistant undermines professional credibility and trust.

### The Solution

IntentGuard uses a tiny DeBERTa-v3-xsmall model (22M parameters, 2.5MB quantized) to classify user intent in <30ms on CPU with three-way classification:

- **Allow** — On-topic legal query, pass to the LLM
- **Deny** — Off-topic, block with a polite redirect
- **Abstain** — Ambiguous, escalate to secondary classifier or human review

---

## Performance

| Metric | Value |
|--------|-------|
| **Overall Accuracy** | 97.9% |
| **Legitimate Block Rate** | 0.00% |
| **Off-Topic Pass Rate** | 0.50% |
| **p99 Latency (CPU)** | <30ms |
| **Model Size (ONNX INT8)** | 2.5MB |
| **Base Parameters** | 22M (DeBERTa-v3-xsmall) |
| **Expected Calibration Error** | <0.03 |

---

## Model Details

| Property | Value |
|----------|-------|
| **Architecture** | DeBERTa-v3-xsmall (fine-tuned for 3-way classification) |
| **Format** | ONNX (INT8 quantized) |
| **Version** | 1.0 |
| **Vertical** | Legal (Law & Compliance) |
| **GPU Required** | No — runs on CPU |
| **Publisher** | [perfecXion.ai](https://perfecxion.ai) |

### Core Topics (Allow)

Contracts, litigation, employment law, intellectual property, criminal law, family law, real estate law, immigration, corporate law, compliance, privacy law, civil rights, estate planning, bankruptcy

### Hard Exclusions (Deny)

Sports, entertainment, cooking, gaming, celebrity gossip, fashion, travel/leisure, fiction writing, relationship advice

---

## Usage

### Python (ONNX Runtime)

```python
import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np

tokenizer = AutoTokenizer.from_pretrained("perfecXion/intentguard-legal")
session = ort.InferenceSession("model.onnx")

text = "What are the requirements for filing a patent application?"
inputs = tokenizer(text, return_tensors="np", max_length=128, truncation=True, padding="max_length")

logits = session.run(None, {
    "input_ids": inputs["input_ids"],
    "attention_mask": inputs["attention_mask"]
})[0]

labels = ["allow", "deny", "abstain"]
prediction = labels[np.argmax(logits)]
confidence = float(np.max(np.exp(logits) / np.sum(np.exp(logits))))

print(f"Intent: {prediction} (confidence: {confidence:.3f})")
# Output: Intent: allow (confidence: 0.996)
```

### Docker

```bash
docker pull ghcr.io/perfecxion/intentguard:legal-1.0
docker run -p 8080:8080 ghcr.io/perfecxion/intentguard:legal-1.0

curl -X POST http://localhost:8080/v1/classify \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "What are my rights as a tenant?"}]}'
```

### pip

```bash
pip install intentguard

from intentguard import IntentGuard
guard = IntentGuard.load("legal")
result = guard.classify("What are the requirements for filing a patent?")
print(result)  # Intent(label='allow', confidence=0.996)
```

---

## Example Classifications

| User Message | Predicted | Confidence | Correct? |
|-------------|-----------|------------|----------|
| "What are the requirements for filing a patent?" | allow | 0.996 | ✅ |
| "Can my landlord evict me without notice?" | allow | 0.995 | ✅ |
| "Who won the Super Bowl?" | deny | 0.999 | ✅ |
| "Tell me a joke" | deny | 0.996 | ✅ |
| "Is GDPR compliance required for US companies?" | allow | 0.989 | ✅ |
| "What's the best recipe for pasta?" | deny | 0.998 | ✅ |

---

## Citation

```bibtex
@misc{thornton2025intentguard,
  title={IntentGuard: A Production-Grade Vertical Intent Classifier for LLM Guardrails},
  author={Thornton, Scott},
  year={2025},
  publisher={perfecXion.ai},
  url={https://perfecxion.ai/articles/intentguard-vertical-intent-classifier-llm-guardrails.html},
  note={Model: https://huggingface.co/perfecXion/intentguard-legal}
}
```

---

## Quality Metrics

| Metric | Result |
|--------|--------|
| Accuracy (Legal vertical) | 97.9% |
| Legitimate Block Rate | 0.00% |
| Off-Topic Pass Rate | 0.50% |
| Expected Calibration Error | <0.03 |
| ONNX INT8 Quantization | Validated |
| CPU Inference (p99) | <30ms |

---

## License

Apache 2.0

---

## Links

- **Research Article**: [IntentGuard: A Production-Grade Vertical Intent Classifier](https://perfecxion.ai/articles/intentguard-vertical-intent-classifier-llm-guardrails.html)
- **Publisher**: [perfecXion.ai](https://perfecxion.ai)
- **Finance Model**: [perfecXion/intentguard-finance](https://huggingface.co/perfecXion/intentguard-finance)
- **Healthcare Model**: [perfecXion/intentguard-healthcare](https://huggingface.co/perfecXion/intentguard-healthcare)
- **Docker Image**: `ghcr.io/perfecxion/intentguard:legal-1.0`