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README.md

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+
+---
+language: en
+tags:
+- vulnerability-detection
+- code-analysis
+- autoencoder
+- anomaly-detection
+library_name: pytorch
+metrics:
+- mse
+---
+
+# CATastrophe - Code Vulnerability Detector
+
+This model is an autoencoder-based vulnerability detector for Python code. It uses TF-IDF
+vectorization and an autoencoder architecture to detect anomalies in code that may indicate
+vulnerabilities.
+
+## Model Details
+
+- **Architecture**: Autoencoder (Input → 512 → 128 → 512 → Input)
+- **Input Features**: 2000 (TF-IDF)
+- **Training Loss**: 0.0005
+- **Framework**: PyTorch
+
+## Usage
+
+```python
+import torch
+import pickle
+from model import Autoencoder
+
+# Load model
+model = Autoencoder(input_dim=2000)
+model.load_state_dict(torch.load('catastrophe_model.pth'))
+model.eval()
+
+# Load vectorizer
+with open('vectorizer.pkl', 'rb') as f:
+    vectorizer = pickle.load(f)
+
+# Analyze code
+code_text = "your code here"
+features = vectorizer.transform([code_text]).toarray()
+features_tensor = torch.tensor(features, dtype=torch.float32)
+
+with torch.no_grad():
+    reconstructed = model(features_tensor)
+    anomaly_score = torch.mean((features_tensor - reconstructed) ** 2, dim=1)
+```
+
+## Training Configuration
+
+- Batch Size: 256
+- Epochs: 50
+- Learning Rate: 0.001
+- Optimizer: Adam
+
+## Limitations
+
+This model is trained on vulnerable commits only and uses reconstruction error as an
+anomaly score. High scores indicate potential vulnerabilities, but manual review is
+recommended.