Datasets:
File size: 2,356 Bytes
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license: mit
task_categories:
- text-classification
tags:
- code
- vulnerability-detection
- embeddings
- codebert
- positive-unlabeled-learning
language:
- code
size_categories:
- 100K<n<1M
---
# PrimeVul CodeBERT Embeddings
Pre-extracted [CLS] token embeddings from microsoft/codebert-base for all functions in the PrimeVul v0.1 vulnerability detection dataset.
## What is this?
Each .npz file contains frozen CodeBERT embeddings (768-dimensional vectors) for C/C++ functions, along with their labels and CWE type annotations. These embeddings were extracted once using a frozen CodeBERT model and are used for downstream PU (positive-unlabeled) learning experiments without requiring GPU access.
## Files
| File | Functions | Vulnerable | Shape |
|------|-----------|-----------|-------|
| train.npz | 175,797 | 4,862 (2.77%) | (175797, 768) |
| valid.npz | 23,948 | 593 (2.48%) | (23948, 768) |
| test.npz | 24,788 | 549 (2.21%) | (24788, 768) |
| test_paired.npz | 870 | 435 (50%) | (870, 768) |
## Arrays in each .npz
- embeddings: (N, 768) float32 -- CodeBERT [CLS] token vectors
- labels: (N,) int32 -- 0 = benign, 1 = vulnerable
- cwe_types: (N,) object -- CWE category string (e.g., "CWE-119") or "unknown"
- idxs: (N,) int64 -- original PrimeVul record index for traceability
## How to use
```python
import numpy as np
data = np.load("train.npz", allow_pickle=True)
X = data["embeddings"] # (175797, 768)
y = data["labels"] # (175797,)
cwes = data["cwe_types"] # (175797,)
```
## Extraction details
- Model: microsoft/codebert-base (RoBERTa architecture, 125M parameters)
- Extraction: frozen model, [CLS] token from final layer
- Tokenization: max_length=512, truncation=True, padding=max_length
- Source data: PrimeVul v0.1 (chronological train/valid/test splits)
- Extracted on: Google Colab, A100 GPU, ~23 minutes for all splits
## Citation
If you use these embeddings, please cite the PrimeVul dataset:
```bibtex
@article{ding2024primevul,
title={Vulnerability Detection with Code Language Models: How Far Are We?},
author={Ding, Yangruibo and Fu, Yanjun and Ibrahim, Omniyyah and Sitawarin, Chawin and Chen, Xinyun and Alomair, Basel and Wagner, David and Ray, Baishakhi and Chen, Yizheng},
journal={arXiv preprint arXiv:2403.18624},
year={2024}
}
```
## License
MIT (same as PrimeVul)
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