Create README.md

README.md

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+---
+language:
+- en
+- code
+tags:
+- security
+- vulnerability-detection
+- codebert
+- classification
+license: mit
+---
+
+# codebert_vulnerability_scanner
+
+## Overview
+
+`codebert_vulnerability_scanner` is a fine-tuned RoBERTa model (specifically based on Microsoft's CodeBERT) designed to detect potential security vulnerabilities in source code snippets. It treats vulnerability detection as a binary classification task, labeling code as either `SAFE` or `VULNERABLE`.
+
+## Model Architecture
+
+This model utilizes the `RobertaForSequenceClassification` architecture. It was pre-trained on the CodeSearchNet dataset (a large collection of function-level code across multiple programming languages) and subsequently fine-tuned on a curated dataset of C and C++ functions labeled with Common Weakness Enumerations (CWEs), such as buffer overflows and memory leaks.
+
+- **Base Model:** `microsoft/codebert-base`
+- **Head:** A linear classification head on top of the pooled output.
+- **Input:** Source code functions (tokenized).
+- **Output:** Logits for two classes: SAFE (0) and VULNERABLE (1).
+
+## Intended Use
+
+This model is intended primarily for DevSecOps workflows and static analysis research.
+
+- **Automated Code Review:** Scanning pull requests for high-risk code patterns before merging.
+- **Security Auditing:** Quickly analyzing large legacy codebases to prioritize manual security reviews.
+- **Research:** Benchmarking against traditional static analysis security testing (SAST) tools.
+
+### How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "your_username/codebert_vulnerability_scanner"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example C function snippet
+code_snippet = """
+void vulnerable_function(char *user_input) {
+    char buffer[64];
+    strcpy(buffer, user_input); // Potential buffer overflow
+}
+"""
+
+inputs = tokenizer(code_snippet, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class_id = logits.argmax().item()
+labels = model.config.id2label
+print(f"Prediction: {labels[predicted_class_id]}")
+# Expected output: VULNERABLE