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README.md

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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)