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README.md

@@ -16,13 +16,11 @@ size_categories:
 
 # PrimeVul CodeBERT Embeddings
 
-Pre-extracted [CLS] token embeddings from microsoft/codebert-base for all functions in the PrimeVul v0.1 vulnerability detection dataset.
+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.
 
-## What is this?
+## 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 embeddings were extracted once using a frozen CodeBERT model and are used for downstream PU (positive-unlabeled) learning experiments without requiring GPU access.
-
-## 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 |
 |------|-----------|-----------|-------|
@@ -31,24 +29,39 @@ Each .npz file contains frozen CodeBERT embeddings (768-dimensional vectors) for
 | test.npz | 24,788 | 549 (2.21%) | (24788, 768) |
 | test_paired.npz | 870 | 435 (50%) | (870, 768) |
 
-## Arrays in each .npz
+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"
+- cwe_types: (N,) U20 string -- CWE category (e.g., "CWE-119") or "unknown"
 - idxs: (N,) int64 -- original PrimeVul record index for traceability
 
-## How to use
+### How to load
 
 ```python
 import numpy as np
 
-data = np.load("train.npz", allow_pickle=True)
+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)
@@ -59,7 +72,7 @@ cwes = data["cwe_types"] # (175797,)
 
 ## Citation
 
-If you use these embeddings, please cite the PrimeVul dataset:
+If you use this data, please cite the PrimeVul dataset:
 
 ```bibtex
 @article{ding2024primevul,