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 CodeBERT Embeddings

Pre-extracted [CLS] token embeddings from microsoft/codebert-base for all functions in the PrimeVul v0.1 vulnerability detection dataset, plus the raw PrimeVul v0.1 JSONL source files.

Embeddings (.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).

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

• 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 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)