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-# TODO
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+# AI Model Evaluation Benchmarks
+
+This chapter is a curated collection of benchmark datasets and evaluation tools designed to assess the capabilities of custom AI models, particularly in domains related to cybersecurity.
+
+The collection is intended to support researchers and developers who are evaluating their own models using reliable, task-specific benchmarks.
+
+Currently, this are the benchmark included:
+
+- [SecEval](https://github.com/XuanwuAI/SecEval)
+- [CyberMetric](https://github.com/CyberMetric)
+
+The goal is to consolidate diverse evaluation tasks under a single framework to support rigorous, standardized testing.
+
+## 🏆 General Summary Table
+
+| Model       | SecEval   | CyberMetric  | Total Value | 
+|-------------|-----------|--------------|-------------|
+| model_name  | `XX.X%`   | `XX.X%`      | `XX.X%`     | 
+
+
+
+## 🔐 SecEval: [https://github.com/XuanwuAI/SecEval](https://github.com/XuanwuAI/SecEval)  
+
+### 📄 Description
+
+SecEval is a benchmark designed to evaluate large language models (LLMs) on security-related tasks. It includes various real-world scenarios such as phishing email analysis, vulnerability classification, and response generation.
+
+### 📥 Installation
+
+```bash
+git clone https://github.com/XuanwuAI/SecEval.git
+cd SecEval
+pip install -r requirements.txt
+```
+### ▶️ Usage
+```bash
+python evaluate.py --model your_model_name --task all
+```
+### 📊 Evaluation Results
+
+| Model Name     | Accuracy | F1 Score | ROUGE | Notes               |
+|----------------|----------|----------|-------|---------------------|
+| GPT-4          | 87.5%    | 84.2%    | 0.61  | Zero-shot           |
+| LLaMA2-13B     | 75.4%    | 71.8%    | 0.52  | Fine-tuned          |
+| Claude 3 Opus  | 79.2%    | 76.5%    | 0.58  | Few-shot setup      |
+| Falcon-40B     | 70.1%    | 68.0%    | 0.47  | Baseline            |
+| YourModel      | XX.X%    | XX.X%    | XX.X  | Custom results here |
+
+📂 Source: results/seceval/scores.csv
+
+---
+
+## 🧠 CyberMetric: [https://github.com/CyberMetric](https://github.com/CyberMetric)  
+
+### 📄 Description 
+CyberMetric is a benchmark framework that focuses on measuring the performance of AI systems in cybersecurity-specific question answering, knowledge extraction, and contextual understanding. It emphasizes both domain knowledge and reasoning ability.
+
+### 📥 Installation
+```bash
+git clone https://github.com/CyberMetric/CyberMetric.git
+cd CyberMetric
+pip install -r requirements.txt
+```
+### ▶️ Usage
+```bash
+python run.py --model your_model_name --task qa
+```
+
+### 📊 Evaluation Results
+
+| Model Name     | Accuracy | F1 Score | ROUGE | Notes               |
+|----------------|----------|----------|-------|---------------------|
+| GPT-4          | 87.5%    | 84.2%    | 0.61  | Zero-shot           |
+| LLaMA2-13B     | 75.4%    | 71.8%    | 0.52  | Fine-tuned          |
+| Claude 3 Opus  | 79.2%    | 76.5%    | 0.58  | Few-shot setup      |
+| Falcon-40B     | 70.1%    | 68.0%    | 0.47  | Baseline            |
+| YourModel      | XX.X%    | XX.X%    | XX.X  | Custom results here |
+📂 Source: results/cybermetric/scores.csv
+