artifacts/README.md

metadata

dataset_name: embedding-cve-nvd-dataset
language:
  - en
license: mit
tags:
  - cybersecurity
  - cve
  - embeddings
  - nvd
pretty_name: CVE NVD Embedding Dataset
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
size_categories:
  - 100K<datasets<1M

CVE NVD Embedding Dataset

This dataset contains the processed CVE/NVD corpus that was used with the rag_mixedbread pipeline. It bundles:

• cve_corpus.jsonl (~700 MB): each line is a JSON object with cve_id, title, description, cvss, vendors, and the pre-computed text chunk that feeds the embedding model.
• decomposed_query_results.json (63 KB): a dictionary of exemplar queries, decomposed sub-questions, and the retrieved doc IDs used for quality checks.

Generation pipeline

1. Raw CVE/NVD feeds were normalized via prepare_cve_corpus.py.
2. Fields were concatenated and deduplicated into retrieval-ready passages.
3. The resulting corpus was used to build MixedBread vector indexes for the RAG workflow.

Usage

You can stream the JSONL file and index it with any vector database:

import json
from datasets import load_dataset

ds = load_dataset("Kushalkhemka/embedding-cve-nvd-dataset", split="train")
for row in ds:
    payload = json.loads(row["text"])  # each row is a JSON line
    # index payload["chunk"] into your vector store

The decomposed_query_results.json file is useful for evaluation—each entry has the original user question, the decomposed sub-queries, and the reference CVE IDs that should match during retrieval.

License

MIT. Please respect the original NVD data terms when redistributing.