README.md

metadata

license: mit
task_categories:
  - text-classification
tags:
  - code
  - vulnerability-detection
  - embeddings
  - codebert
  - positive-unlabeled-learning
language:
  - code
size_categories:
  - 100K<n<1M

PrimeVul Embeddings for PU Learning

Pre-extracted [CLS] token embeddings from two code models for all functions in the PrimeVul v0.1 vulnerability detection dataset, plus the raw PrimeVul v0.1 JSONL source files.

CodeBERT Embeddings (root .npz files)

Each .npz file contains frozen CodeBERT embeddings (768-dimensional vectors) for C/C++ functions, along with their labels and CWE type annotations. These were extracted once using a frozen CodeBERT model and are used for downstream PU (positive-unlabeled) learning experiments without requiring GPU access.

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,) U20 string -- CWE category (e.g., "CWE-119") or "unknown"
• idxs: (N,) int64 -- original PrimeVul record index for traceability

How to load

import numpy as np

data = np.load("train.npz")
X = data["embeddings"]  # (175797, 768)
y = data["labels"]       # (175797,)
cwes = data["cwe_types"] # (175797,)

No special flags needed. All arrays use standard numpy dtypes (float32, int32, U20, int64).

VulBERTa Embeddings (vulberta/ folder)

Same format as CodeBERT but extracted from claudios/VulBERTa-mlm, a RoBERTa model pretrained on C/C++ vulnerability code. Same functions, same labels, same idxs -- only the embedding vectors differ.

File	Functions	Shape
vulberta/train.npz	175,797	(175797, 768)
vulberta/valid.npz	23,948	(23948, 768)
vulberta/test.npz	24,788	(24788, 768)
vulberta/test_paired.npz	870	(870, 768)

VulBERTa embeddings have higher L2 magnitude (~27 vs ~21 for CodeBERT) but the same 768 dimensions. Load the same way: np.load("vulberta/train.npz").

Raw PrimeVul v0.1 data (raw/ folder)

The raw/ folder contains the original PrimeVul v0.1 JSONL files from the PrimeVul project. Each line is a JSON object with fields including func (source code), target (0/1 label), cwe (list of CWE strings), cve (CVE identifier), and project metadata.

File	Records
raw/primevul_train.jsonl	175,797
raw/primevul_valid.jsonl	23,948
raw/primevul_test.jsonl	24,788
raw/primevul_train_paired.jsonl	9,724
raw/primevul_valid_paired.jsonl	870
raw/primevul_test_paired.jsonl	870

Extraction details

CodeBERT

• 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

VulBERTa

• Model: claudios/VulBERTa-mlm (RoBERTa architecture, 125M parameters, pretrained on C/C++ vulnerability code)
• Extraction: frozen model, [CLS] token from final layer
• Tokenization: max_length=512, truncation=True, padding=max_length
• Source data: PrimeVul v0.1 (same functions as CodeBERT)
• Extracted on: Google Colab, A100 GPU, ~23 minutes for all splits

Citation

If you use this data, please cite the PrimeVul dataset:

@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)