Add dataset card

README.md

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+
+
+---
+language: 
+- en
+tags:
+- vulnerability-detection
+- cve
+- code-changes
+- software-security
+license: mit
+dataset_info:
+  features:
+    - name: CVE_ID
+      dtype: string
+    - name: CWE_ID
+      dtype: string
+    - name: Score
+      dtype: float
+    - name: Summary
+      dtype: string
+    - name: commit_id
+      dtype: string
+    - name: codeLink
+      dtype: string
+    - name: file_name
+      dtype: string
+    - name: func_after
+      dtype: string
+    - name: lines_after
+      dtype: string
+  dataset_size: 10GB (extracted)
+---
+
+# MSR Data Cleaned - C/C++ Code Vulnerability Dataset
+
+## 📌 Dataset Description
+A curated collection of C/C++ code vulnerabilities paired with:
+- CVE details (scores, classifications, exploit status)
+- Code changes (commit messages, added/deleted lines)
+- File-level and function-level diffs
+
+## 🔍 Sample Data Structure
+```python
++---------------+-----------------+----------------------+---------------------------+
+| CVE ID        | Attack Origin   | Publish Date         | Summary                   |
++===============+=================+======================+===========================+
+| CVE-2015-8467 | Remote          | 2015-12-29           | "The samldb_check_user..."|
++---------------+-----------------+----------------------+---------------------------+
+| CVE-2016-1234 | Local           | 2016-01-15           | "Buffer overflow in..."   |
++---------------+-----------------+----------------------+---------------------------+
+```
+
+## 🛠️ Usage Instructions
+
+### 1. Accessing in Colab
+```python
+!pip install huggingface_hub -q
+from huggingface_hub import snapshot_download
+
+repo_id = "starsofchance/MSR_data_cleaned"
+dataset_path = snapshot_download(repo_id=repo_id, repo_type="dataset")
+```
+
+### 2. Extracting the Dataset
+```python
+!apt-get install unzip -qq
+!unzip "/root/.cache/huggingface/.../MSR_data_cleaned.zip" -d "/content/extracted_data"
+```
+**Note:** Extracted size is **10GB** (1.5GB compressed).
+
+### 3. Creating Splits (Colab Pro Recommended)
+We used this memory-efficient approach:
+```python
+from datasets import load_dataset
+dataset = load_dataset("csv", data_files="MSR_data_cleaned.csv", streaming=True)
+
+# Randomly distribute rows (80-10-10)
+for row in dataset:
+    rand = random.random()
+    if rand < 0.8: write_to(train.csv)
+    elif rand < 0.9: write_to(validation.csv)
+    else: write_to(test.csv)
+```
+**Hardware Requirements:**
+- Minimum 25GB RAM
+- Strong CPU (Colab Pro T4 GPU recommended)
+
+## 📜 Citation
+```bibtex
+@inproceedings{fan2020ccode,
+  title={A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries},
+  author={Fan, Jiahao and Li, Yi and Wang, Shaohua and Nguyen, Tien N},
+  booktitle={MSR '20: 17th International Conference on Mining Software Repositories},
+  pages={1--5},
+  year={2020},
+  doi={10.1145/3379597.3387501}
+}
+```
+
+## 🌟 Dataset Creation
+- **Source**: Original data from [MSR 2020 Paper](https://doi.org/10.1145/3379597.3387501)
+- **Processing**:
+  - Cleaned and standardized CSV format
+  - Stream-based splitting to handle large size
+  - Preserved all original metadata
+