mirror of https://github.com/aliasrobotics/cai.git
75 lines
2.9 KiB
Markdown
75 lines
2.9 KiB
Markdown
# AI Model Evaluation Benchmarks
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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.
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The collection is intended to support researchers and developers who are evaluating their own models using reliable, task-specific benchmarks.
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Currently, this are the benchmarks included:
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- [SecEval](https://github.com/XuanwuAI/SecEval)
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- [CyberMetric](https://github.com/CyberMetric)
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The goal is to consolidate diverse evaluation tasks under a single framework to support rigorous, standardized testing.
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## 🏆 General Summary Table
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| Model | SecEval | CyberMetric | Total Value |
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|-------------|-----------|--------------|-------------|
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| model_name | `XX.X%` | `XX.X%` | `XX.X%` |
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## 🔐 [SecEval](https://github.com/XuanwuAI/SecEval)
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#### 📄 Description
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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.
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#### ▶️ Usage
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```bash
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cd benchmarks/seceval/eval
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pip install -r requirements.txt
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```
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```bash
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python3 eval.py --dataset_file datasets/questions.json --output_dir outputs --backend ollama --model ollama/qwen2.5:14b
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```
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#### 📊 Evaluation Results
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| Model Name | Accuracy | F1 Score | ROUGE | Notes |
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|----------------|----------|----------|-------|---------------------|
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| GPT-4 | 87.5% | 84.2% | 0.61 | Zero-shot |
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| LLaMA2-13B | 75.4% | 71.8% | 0.52 | Fine-tuned |
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| Claude 3 Opus | 79.2% | 76.5% | 0.58 | Few-shot setup |
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| Falcon-40B | 70.1% | 68.0% | 0.47 | Baseline |
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| YourModel | XX.X% | XX.X% | XX.X | Custom results here |
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---
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## 🧠 [CyberMetric](https://github.com/CyberMetric)
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#### 📄 Description
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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.
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#### ▶️ Usage
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```bash
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cd benchmarks/cybermetric
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```
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```bash
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python CyberMetric_evaluator.py --model_name ollama/qwen2.5:14b --file_path CyberMetric-2-v1.json
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```
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#### 📊 Evaluation Results
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| Model Name | Accuracy | F1 Score | ROUGE | Notes |
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|----------------|----------|----------|-------|---------------------|
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| GPT-4 | 87.5% | 84.2% | 0.61 | Zero-shot |
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| LLaMA2-13B | 75.4% | 71.8% | 0.52 | Fine-tuned |
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| Claude 3 Opus | 79.2% | 76.5% | 0.58 | Few-shot setup |
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| Falcon-40B | 70.1% | 68.0% | 0.47 | Baseline |
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| YourModel | XX.X% | XX.X% | XX.X | Custom results here |
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📂 Source: results/cybermetric/scores.csv
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