Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- mahdin70/cwe_enriched_balanced_bigvul_primevul
+metrics:
+- accuracy
+- precision
+- f1
+- recall
+base_model:
+- microsoft/graphcodebert-base
+library_name: transformers
+---
+
+# GraphCodeBERT-VulnCWE - Fine-Tuned GraphCodeBERT for Vulnerability and CWE Classification
+
+## Model Overview
+This model is a fine-tuned version of **microsoft/graphcodebert-base** on a curated and enriched dataset for vulnerability detection and CWE classification. It is capable of predicting whether a given code snippet is vulnerable and, if vulnerable, identifying the specific CWE ID associated with it.
+
+## Dataset
+The model was fine-tuned using the dataset [mahdin70/cwe_enriched_balanced_bigvul_primevul](https://huggingface.co/datasets/mahdin70/cwe_enriched_balanced_bigvul_primevul). The dataset contains both vulnerable and non-vulnerable code samples and is enriched with CWE metadata.
+
+### CWE IDs Covered:
+1. **CWE-119**: Improper Restriction of Operations within the Bounds of a Memory Buffer  
+2. **CWE-20**: Improper Input Validation  
+3. **CWE-125**: Out-of-bounds Read  
+4. **CWE-399**: Resource Management Errors  
+5. **CWE-200**: Information Exposure  
+6. **CWE-787**: Out-of-bounds Write  
+7. **CWE-264**: Permissions, Privileges, and Access Controls  
+8. **CWE-416**: Use After Free  
+9. **CWE-476**: NULL Pointer Dereference  
+10. **CWE-190**: Integer Overflow or Wraparound  
+11. **CWE-189**: Numeric Errors  
+12. **CWE-362**: Concurrent Execution using Shared Resource with Improper Synchronization  
+
+---
+
+## Model Training
+The model was trained for **3 epochs** with the following configuration:
+- **Learning Rate**: 2e-5  
+- **Weight Decay**: 0.01  
+- **Batch Size**: 8  
+- **Optimizer**: AdamW  
+- **Scheduler**: Linear  
+
+### Training Loss and Validation Metrics Per Epoch:
+| Epoch | Training Loss | Validation Loss | Vul Accuracy | Vul Precision | Vul Recall | Vul F1 | CWE Accuracy |
+|-------|---------------|-----------------|--------------|---------------|------------|--------|--------------|
+| 1     | 1.2824        | 1.4160          | 0.7914       | 0.8990        | 0.5200     | 0.6589 | 0.3551       |
+| 2     | 1.1292        | 1.2632          | 0.8007       | 0.8037        | 0.6426     | 0.7142 | 0.4433       |
+| 3     | 0.8598        | 1.2436          | 0.7945       | 0.7669        | 0.6747     | 0.7179 | 0.4605       |
+
+#### Training Summary:
+- **Total Training Steps**: 5916  
+- **Training Loss**: 1.2380  
+- **Training Time**: 4785.0 seconds (~80 minutes)  
+- **Training Speed**: 9.89 samples per second  
+- **Steps Per Second**: 1.236  
+
+
+## How to Use the Model
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained("mahdin70/GraphCodeBERT-VulnCWE", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/graphcodebert-base")
+
+code_snippet = "int main() { int arr[10]; arr[11] = 5; return 0; }"
+inputs = tokenizer(code_snippet, return_tensors="pt")
+outputs = model(**inputs)
+
+vul_logits = outputs["vul_logits"]
+cwe_logits = outputs["cwe_logits"]
+
+vul_pred = vul_logits.argmax(dim=1).item()
+cwe_pred = cwe_logits.argmax(dim=1).item()
+
+print(f"Vulnerability: {'Vulnerable' if vul_pred == 1 else 'Non-vulnerable'}")
+print(f"CWE ID: {cwe_pred if vul_pred == 1 else 'N/A'}")
+```