---
license: mit
tags:
- cybersecurity
- africa
- threat-detection
- NLP
- Allsafeafrica
- cyber-aware
datasets:
- HuggingFaceFW/fineweb-2
metrics:
- accuracy
- bertscore
base_model:
- HuggingFaceTB/SmolLM3-3B
- google/gemma-3n-E4B-it
new_version: HuggingFaceTB/SmolLM3-3B
pipeline_tag: text-classification
library_name: adapter-transformers
---

# 🛡️ Cyber Threat Detector Africa

> Developed by [Allsafeafrica](https://huggingface.co/allsafeafrica)  
> A lightweight NLP model built to detect and classify potential cybersecurity threats in textual data across African SMEs, startups, and digital communities.

---

## 📌 Overview

**Cyber Threat Detector Africa** is an AI-powered model designed to:
- Classify cyber risk indicators in natural language (emails, messages, reports)
- Support awareness in employee training platforms
- Act as a backend tool for ESG-cyber hybrid security assessments

---

## 🧠 Model Info

| Attribute | Detail |
|----------|--------|
| Framework | `transformers`, `pytorch` |
| Base Model | `distilbert-base-uncased` |
| Fine-tuned On | Synthetic + local African threat incident data |
| Labels | `phishing`, `malware`, `social-engineering`, `safe`, `suspicious` |
| Accuracy | ~91.7% on test set |

---

## ✨ Example Usage

```python
from transformers import pipeline

threat_detector = pipeline("text-classification", model="allsafeafrica/cyber-threat-detector-africa")

text = "Your account has been suspended. Click here to verify your identity."
threat_detector(text)