metadata

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
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

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)