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README.md

@@ -14,11 +14,11 @@ size_categories:
   - 100K<n<1M
 ---
 
-# PrimeVul CodeBERT Embeddings
+# PrimeVul Embeddings for PU Learning
 
-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.
+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.
 
-## Embeddings (.npz 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.
 
@@ -49,6 +49,19 @@ 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.
@@ -64,12 +77,22 @@ The raw/ folder contains the original PrimeVul v0.1 JSONL files from the PrimeVu
 
 ## 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: