Update dataset card

README.md

@@ -1,30 +1,70 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: title
-    dtype: string
-  - name: description
-    dtype: string
-  - name: severity
-    dtype: string
-  - name: cve_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 47143216
-    num_examples: 115068
-  - name: test
-    num_bytes: 5238408
-    num_examples: 12786
-  download_size: 24844204
-  dataset_size: 52381624
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+language:
+- zh
+license: gpl-3.0
+tags:
+- vulnerability
+- cybersecurity
+- cnvd
+- severity-classification
+size_categories:
+- 100K-1M
 ---
+
+# Vulnerability-CNVD
+
+Vulnerability descriptions and severity labels from the [China National Vulnerability Database (CNVD)](https://www.cnvd.org.cn/), extracted via [Vulnerability-Lookup](https://vulnerability.circl.lu).
+
+## Dataset structure
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | CNVD identifier (e.g., CNVD-2025-03529) |
+| `title` | string | Vulnerability title in Chinese |
+| `description` | string | Vulnerability description in Chinese |
+| `severity` | string | Severity level: 高 (High), 中 (Medium), or 低 (Low) |
+| `cve_id` | string | Corresponding CVE identifier, if available (empty string if none) |
+
+## Severity distribution
+
+The dataset is imbalanced:
+
+| Severity | Chinese | Approximate share |
+|----------|---------|-------------------|
+| High | 高 | ~36% |
+| Medium | 中 | ~55% |
+| Low | 低 | ~9% |
+
+## CVE overlap
+
+Approximately 81% of CNVD entries have a corresponding CVE identifier. The overlap rate varies by year:
+
+- **2020-2021**: 68-69% CVE mapping rate
+- **2022+**: 91-97% CVE mapping rate
+
+The ~19% of CNVD-only entries are concentrated in Chinese domestic software (PHP CMS, ERP systems). Western vendors (Adobe, Microsoft, IBM, Cisco) are largely absent from the CNVD-only subset.
+
+## Coverage and provenance
+
+CNVD reserves 50,000-100,000 vulnerability IDs per year but publishes full details for only a fraction. The publication rate has declined significantly:
+
+- **2015**: ~94% of reserved IDs have published details
+- **2023**: ~4% of reserved IDs have published details
+
+This decline coincides with China's Regulations on the Management of Security Vulnerabilities (RMSV), effective September 2021.
+
+Entries without a description or severity label are excluded from this dataset.
+
+## Duplicate descriptions
+
+CNVD reuses boilerplate descriptions across different vulnerability IDs (product-specific entries sharing the same text). When using this dataset for train/test splits, **split on unique description text** rather than on IDs to avoid data leakage. See [VulnTrain#19](https://github.com/vulnerability-lookup/VulnTrain/issues/19) for details.
+
+## Source
+
+- **Data source**: [Vulnerability-Lookup](https://vulnerability.circl.lu) API
+- **Extraction tool**: [VulnTrain](https://github.com/vulnerability-lookup/VulnTrain)
+- **Original source**: [CNVD](https://www.cnvd.org.cn/)
+
+## Related models
+
+- [CIRCL/vulnerability-severity-classification-chinese-macbert-base](https://huggingface.co/CIRCL/vulnerability-severity-classification-chinese-macbert-base) — severity classifier trained on this dataset