Add dataset card

README.md

@@ -1,4 +1,19 @@
 ---
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- text-classification
+- text-generation
+tags:
+- cybersecurity
+- cve
+- cwe
+- vulnerability
+- security
+pretty_name: CVE-to-CWE Consensus
+size_categories:
+- 100K<n<1M
 dataset_info:
   features:
   - name: messages
@@ -29,3 +44,87 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# CVE-to-CWE Consensus Dataset
+
+A multi-label dataset mapping **CVE vulnerability descriptions** to their **CWE weakness type(s)**, built for fine-tuning instruction-tuned LLMs (e.g. with [Unsloth](https://unsloth.ai)). Each label is a **consensus** assignment: a CWE is kept only when **NVD and the CVE Numbering Authority (CNA) independently agree** on it, after rolling both up to **CWE View-1003** (the ~130-weakness "Weaknesses for Simplified Mapping of Published Vulnerabilities").
+
+## TL;DR
+
+- **Task:** given a CVE description, predict the CWE ID(s) — multi-label.
+- **116,793 examples** — train 91,862 / validation 13,705 / test 11,226.
+- **Format:** conversational `messages` (system / user / assistant), ready for chat-template SFT.
+- **Trust model:** labels are the **intersection** of two independent official sources (NVD + CNA), so single-source errors are excluded by construction.
+
+## Example row
+
+```json
+{"messages": [
+  {"role": "system", "content": "You are a vulnerability analyst. Given a CVE description, reply with only the CWE ID(s) it maps to, comma-separated."},
+  {"role": "user", "content": "A reflected cross-site scripting issue in Acme Portal lets a remote attacker inject arbitrary script via the q parameter."},
+  {"role": "assistant", "content": "CWE-79"}
+]}
+```
+
+Multi-label targets are comma-separated and numerically sorted, e.g. `"CWE-79, CWE-352"`. The model input is the **description only** — CVE IDs and source label fields are never included, to avoid memorization/leakage.
+
+## How it was built
+
+1. **Sources (official upstreams only):**
+   - [NVD CVE API 2.0](https://nvd.nist.gov/developers/vulnerabilities) — analyst-assigned CWEs (`weaknesses`).
+   - [CVEProject/cvelistV5](https://github.com/CVEProject/cvelistV5) — CNA-supplied CWEs (`problemTypes`) and the English description.
+   - [MITRE CWE](https://cwe.mitre.org) catalog (v4.20) — View-1003 membership + the `ChildOf` hierarchy.
+2. **Roll-up:** every assigned CWE is rolled up to its nearest View-1003 mapping ancestor(s).
+3. **Consensus:** the label is the **intersection** of NVD's and the CNA's rolled-up CWE sets; CVEs with no agreement are dropped.
+4. **Filtering:** REJECTED/DISPUTED CVEs, non-English and too-short (<40 char) descriptions are dropped; pseudo-labels (`NVD-CWE-noinfo`/`-Other`) are excluded.
+5. **De-duplication:** near-duplicate descriptions (MinHash, Jaccard ≥ 0.7) are grouped into families, and **whole families stay within a single split**, so near-identical text cannot leak across train/validation/test (verified: 0 shared descriptions across splits).
+6. **Split:** dedup-family-aware, ~80/10/10.
+
+## Provenance (reproducibility)
+
+| Source | Version |
+|---|---|
+| cvelistV5 | commit `7f860dcfb8260ccff2f23b4b5f685303fd314f17` |
+| NVD | pulled 2026-05-29 |
+| MITRE CWE | v4.20 |
+
+## Statistics
+
+- **Keep rate:** 116,793 of 354,163 scanned CVEs (~33%); most drops are "no consensus" (sources disagreed, or only one assigned a CWE).
+- **Recency skew:** consensus concentrates on **2023+** CVEs, because that is when CNAs began reliably populating CWE — this is the main coverage bias.
+- **Label space:** 127 distinct View-1003 CWEs appear in the consensus set; a minimum-examples floor (50) prunes the rarest, leaving ~117 effective labels. The distribution is long-tailed (CWE-79 most common).
+- **Cardinality:** most examples carry a single label; ~14% carry two or more.
+
+## Intended use
+
+Fine-tuning or evaluating models that classify CVE descriptions into CWE types (vulnerability triage, enrichment, prioritization aids). Held-out `test` is intended for measuring multi-label performance (e.g. exact-match and micro/macro F1).
+
+## Limitations & biases
+
+- **Recency-skewed (2023+)** — weaker coverage of older CVE styles.
+- **Consensus is high-precision, not exhaustive** — CVEs where the two sources disagree (often genuinely ambiguous) are excluded, so this set is "cleaner/easier" than the full CVE population.
+- **Roll-up to View-1003** discards sub-View-1003 specificity (variant-level CWEs map to their base).
+- **English only.**
+- Not a substitute for analyst review on novel or complex vulnerabilities.
+
+## License & attribution
+
+Released under **CC BY 4.0**. The dataset derives from public sources; please also honor their terms:
+
+- **NVD** (NIST) — U.S. Government work, public domain.
+- **CVE Program / cvelistV5** — © the CVE Program; CVE Records are free to use with attribution.
+- **MITRE CWE** — © The MITRE Corporation; free to use with attribution.
+
+CWE™ and CVE® are trademarks of The MITRE Corporation.
+
+## Citation
+
+```bibtex
+@misc{cve_cwe_consensus_2026,
+  title        = {CVE-to-CWE Consensus Dataset},
+  author       = {eiphuggincve},
+  year         = {2026},
+  howpublished = {Hugging Face Hub},
+  url          = {https://huggingface.co/datasets/eiphuggincve/cve-cwe-consensus}
+}
+```