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README.md

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----
-# YAML Metadata Block
-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
----
-
-# MSR Data Cleaned - C/C++ Code Vulnerability Dataset
-
-[![Dataset License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
-
-
-
-## 📌 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
-