mahdin70
/

codebert-devign-code-vulnerability-detector

@@ -1,199 +1,121 @@
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 ---
 library_name: transformers
+tags:
+- Code
+- Vulnerability
+- Detection
+datasets:
+- DetectVul/devign
+language:
+- en
+base_model:
+- microsoft/codebert-base
+license: mit
+metrics:
+- accuracy
+- precision
+- f1
+- recall
 ---
+## CodeBERT for Code Vulnerability Detection
+## Model Summary
+This model is a fine-tuned version of **microsoft/codebert-base**, optimized for detecting vulnerabilities in code. It is trained on the **DetectVul/devign** dataset. The model takes in a code snippet and classifies it as either **safe (0)** or **vulnerable (1)**.
 ## Model Details
+- **Developed by:** Mukit Mahdin
+- **Finetuned from:** `microsoft/codebert-base`
+- **Language(s):** English (for code comments & metadata), C/C++
+- **License:** MIT
+- **Task:** Code vulnerability detection
+- **Dataset Used:** `DetectVul/devign`
+- **Architecture:** Transformer-based sequence classification
 ## Uses
 ### Direct Use
+This model can be used for **static code analysis**, security audits, and automatic vulnerability detection in software repositories. It is useful for:
+- **Developers**: To analyze their code for potential security flaws.
+- **Security Teams**: To scan repositories for known vulnerabilities.
+- **Researchers**: To study vulnerability detection in AI-powered systems.
+### Downstream Use
+This model can be integrated into **IDE plugins**, **CI/CD pipelines**, or **security scanners** to provide real-time vulnerability detection.
 ### Out-of-Scope Use
+- The model is **not meant to replace human security experts**.
+- It may not generalize well to **languages other than C/C++**.
+- False positives/negatives may occur due to dataset limitations.
 ## Bias, Risks, and Limitations
+- **False Positives & False Negatives:** The model may flag safe code as vulnerable or miss actual vulnerabilities.
+- **Limited to C/C++:** The model was trained on a dataset primarily composed of **C and C++ code**. It may not perform well on other languages.
+- **Dataset Bias:** The training data may not cover all possible vulnerabilities.
+### Recommendations
+Users should **not rely solely on the model** for security assessments. Instead, it should be used alongside **manual code review and static analysis tools**.
+## How to Get Started with the Model
+Use the code below to load the model and run inference on a sample code snippet:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# Load the fine-tuned model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
+model = AutoModelForSequenceClassification.from_pretrained("mahdin70/codebert-devign-code-vulnerability-detector")
+# Sample code snippet
+code_snippet = '''
+void process(char *input) {
+    char buffer[50];
+    strcpy(buffer, input); // Potential buffer overflow
+}
+'''
+# Tokenize the input
+inputs = tokenizer(code_snippet, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_label = torch.argmax(predictions, dim=1).item()
+# Output the result
+print("Vulnerable Code" if predicted_label == 1 else "Safe Code")
+```
 ## Training Details
 ### Training Data
+- **Dataset:** `DetectVul/devign`
+- **Classes:** `0 (Safe)`, `1 (Vulnerable)`
 ### Training Procedure
+- **Optimizer:** AdamW
+- **Loss Function:** CrossEntropyLoss
+- **Batch Size:** 8
+- **Learning Rate:** 2e-05
+- **Epochs:** 3
+- **Hardware Used:** T4 GPU
+### Metrics
+| Metric  | Score |
+|------------|-------------|
+| **Train Loss** | 0.5898 |
+| **Evaluation Loss** | 0.6153 |
+| **Accuracy** | 64.09% |
+| **F1 Score** | 46.42% |
+| **Precision** | 73.78% |
+| **Recall** | 33.86% |
 ## Environmental Impact
+| Factor  | Value |
+|-----------|----------|
+| **GPU Used** | T4 GPU |
+| **Training Time** | ~1 hour |