---
library_name: onnx
pipeline_tag: text-classification
license: apache-2.0
language:
  - en
tags:
  - intentguard
  - guardrails
  - llm-safety
  - content-moderation
  - healthcare
  - deberta-v2
  - onnx-runtime
  - intent-classification
  - chatbot-security
model-index:
  - name: intentguard-healthcare
    results:
      - task:
          type: text-classification
          name: Intent Classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 98.9
          - name: Legitimate Block Rate
            type: accuracy
            value: 0.0
          - name: Off-Topic Pass Rate
            type: accuracy
            value: 0.98
---

# IntentGuard — Healthcare & Clinical

[![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-98.9%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 healthcare chatbots on-topic and compliant.**

[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% | This model |
| **intentguard-legal** | Legal & Compliance | 97.9% | 0.50% | [perfecXion/intentguard-legal](https://huggingface.co/perfecXion/intentguard-legal) |

---

## Overview

### The Problem

Healthcare chatbots face unique regulatory risks — HIPAA compliance, patient safety, and liability exposure demand that AI assistants stay strictly within clinical domains. Users asking about sports scores, celebrity news, or relationship advice should be blocked, not answered.

### 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 healthcare 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** | 98.9% |
| **Legitimate Block Rate** | 0.00% |
| **Off-Topic Pass Rate** | 0.98% |
| **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** | Healthcare (Clinical & Wellness) |
| **GPU Required** | No — runs on CPU |
| **Publisher** | [perfecXion.ai](https://perfecxion.ai) |

### Core Topics (Allow)

Symptoms, diagnosis, treatment, medications, preventive care, nutrition, mental health, fitness, chronic conditions, surgery, emergency care, health insurance, patient rights, telemedicine

### 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-healthcare")
session = ort.InferenceSession("model.onnx")

text = "What are the symptoms of Type 2 diabetes?"
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: 0.997)
```

### Docker

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

curl -X POST http://localhost:8080/v1/classify \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "What medications interact with metformin?"}]}'
```

### pip

```bash
pip install intentguard

from intentguard import IntentGuard
guard = IntentGuard.load("healthcare")
result = guard.classify("What are the symptoms of Type 2 diabetes?")
print(result)  # Intent(label='allow', confidence=0.997)
```

---

## Example Classifications

| User Message | Predicted | Confidence | Correct? |
|-------------|-----------|------------|----------|
| "What are the symptoms of Type 2 diabetes?" | allow | 0.997 | ✅ |
| "What medications interact with metformin?" | allow | 0.996 | ✅ |
| "Who won the Super Bowl?" | deny | 0.999 | ✅ |
| "Tell me a joke" | deny | 0.996 | ✅ |
| "Is telemedicine covered by Medicare?" | allow | 0.981 | ✅ |
| "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-healthcare}
}
```

---

## Quality Metrics

| Metric | Result |
|--------|--------|
| Accuracy (Healthcare vertical) | 98.9% |
| Legitimate Block Rate | 0.00% |
| Off-Topic Pass Rate | 0.98% |
| 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)
- **Legal Model**: [perfecXion/intentguard-legal](https://huggingface.co/perfecXion/intentguard-legal)
- **Docker Image**: `ghcr.io/perfecxion/intentguard:healthcare-1.0`