Update README.md

README.md

@@ -1,57 +1,83 @@
 ---
+license: mit
+language:
+- code
 library_name: transformers
-base_model: microsoft/codebert-base
 tags:
-- generated_from_trainer
-model-index:
-- name: vuln-detector-codebert
-  results: []
+- text-classification
+- code-classification
+- vulnerability-detection
+- automatic-vulnerability-detection
+- secure-coding
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Vulnerability Detector for C Code (SARD)
 
-# vuln-detector-codebert
+This model is a fine-tuned version of `microsoft/codebert-base` designed to detect vulnerabilities in C source code functions. It was developed as a submission for the AI Grand Challenge (PS-1).
 
-This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0000
+## Model Description
 
-## Model description
+This is a binary text-classification model that takes a C function as input and classifies it as either **Vulnerable** (`LABEL_1`) or **Safe** (`LABEL_0`).
 
-More information needed
+The model was specifically fine-tuned on the [NIST SARD (Software Assurance Reference Dataset)](https://samate.nist.gov/SARD/), focusing on common C vulnerabilities like Memory Leaks, Buffer Overflows, and other CWEs present in the Juliet Test Suite. Due to the clean and structured nature of the SARD dataset, the model achieved a very high accuracy on the validation set.
 
-## Intended uses & limitations
+## Intended Uses & Limitations
 
-More information needed
+This model is intended as a proof-of-concept tool to assist developers in identifying potentially vulnerable code patterns during the development lifecycle.
 
-## Training and evaluation data
+**Limitations:**
+*   The model is highly specialized for the types of vulnerabilities found in the SARD dataset. Its performance on real-world, messy, or obfuscated code may be lower.
+*   It should be used as an assistive tool, not as a replacement for comprehensive security audits or other static analysis tools.
+*   The model classifies entire functions and may not pinpoint the exact line of code responsible for the vulnerability.
 
-More information needed
+## How to Use
 
-## Training procedure
+The model can be easily used with the `transformers` library `pipeline`.
 
-### Training hyperparameters
+```python
+from transformers import pipeline
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 1
+# Load the classifier pipeline
+classifier = pipeline("text-classification", model="jacpacd/vuln-detector-codebert-c-sard")
 
-### Training results
+# Example of a vulnerable C function (Memory Leak)
+vulnerable_code = """
+void CWE401_Memory_Leak__strdup_char_01_bad()
+{
+    char * data;
+    data = NULL;
+    {
+        char myString[] = "myString";
+        /* POTENTIAL FLAW: Allocate memory from the heap */
+        data = strdup(myString);
+        printLine(data);
+    }
+    /* POTENTIAL FLAW: No deallocation of memory */
+    ;
+}
+"""
 
-| Training Loss | Epoch | Step  | Validation Loss |
-|:-------------:|:-----:|:-----:|:---------------:|
-| 0.0           | 1.0   | 17310 | 0.0000          |
+# Example of a safe C function
+safe_code = """
+void CWE401_Memory_Leak__strdup_char_01_goodB2G()
+{
+    char * data;
+    data = NULL;
+    {
+        char myString[] = "myString";
+        data = strdup(myString);
+        printLine(data);
+    }
+    /* FIX: Deallocate memory */
+    free(data);
+}
+"""
 
+results_vuln = classifier(vulnerable_code)
+results_safe = classifier(safe_code)
 
-### Framework versions
+print(f"Vulnerable Code Prediction: {results_vuln[0]}")
+# Expected output: {'label': 'LABEL_1', 'score': 0.99...}
 
-- Transformers 4.56.0
-- Pytorch 2.8.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.0
+print(f"Safe Code Prediction: {results_safe[0]}")
+# Expected output: {'label': 'LABEL_0', 'score': 0.99...}