Add CVSS v3 combined train and test splits derived from CIRCL/vulnerability-scores

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-classification
+language:
+- en
+pretty_name: Vulnerability scores (CVSS v3 combined)
+size_categories:
+- 100K<n<1M
+tags:
+- vulnerability
+- cybersecurity
+- security
+- cve
+- cvss
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: description
+    dtype: string
+  - name: cpes
+    sequence: string
+  - name: cvss_v4_0
+    dtype: float64
+  - name: cvss_v3_1
+    dtype: float64
+  - name: cvss_v3_0
+    dtype: float64
+  - name: cvss_v2_0
+    dtype: float64
+  - name: patch_commit_url
+    dtype: string
+  - name: source
+    dtype: string
+  - name: cvss_v3_1_v3_combined
+    dtype: float64
+  - name: severity_band
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 144500000
+    num_examples: 509067
+  - name: test
+    num_bytes: 16100000
+    num_examples: 56502
+  download_size: 160600000
+  dataset_size: 160600000
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+---
+
+# Vulnerability scores (CVSS v3 combined)
+
+A labeled slice of [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores) for training and evaluating models that predict CVSS v3 severity from a vulnerability description.
+
+Every row has a combined v3 score and a severity band. Rows with no v3.1 or v3.0 score were dropped.
+
+## What changed from the original
+
+The CIRCL dataset stores four separate CVSS columns (`cvss_v4_0`, `cvss_v3_1`, `cvss_v3_0`, `cvss_v2_0`). Those versions are not on the same scale, so this derivative does **not** average them or prefer v4.
+
+`cvss_v3_1_v3_combined` is:
+
+1. `cvss_v3_1` when it is present
+2. otherwise `cvss_v3_0`
+
+v3.0 and v3.1 are the same scoring system (mean absolute difference about 0.09 on overlapping rows). v2 and v4 are not mixed in.
+
+`severity_band` is the standard CVSS qualitative rating of that combined score:
+
+| Band | Score |
+| --- | --- |
+| `low` | 0.0–3.9 |
+| `medium` | 4.0–6.9 |
+| `high` | 7.0–8.9 |
+| `critical` | 9.0–10.0 |
+
+Unlabeled rows are removed. That includes rows with no CVSS score at all, and rows that only have a v2 or v4 score.
+
+Original columns are kept, including the unused v2/v4 values.
+
+Train and test splits follow CIRCL’s split. A row is not moved between splits.
+
+## Splits
+
+| Split | Rows | From v3.1 | From v3.0 fallback |
+| --- | ---: | ---: | ---: |
+| train | 509,067 | 410,050 | 99,017 |
+| test | 56,502 | 45,623 | 10,879 |
+| total | 565,569 | 455,673 | 109,896 |
+
+CIRCL’s full dump is 779,178 rows. This set keeps 565,569 (72.5%).
+
+### Severity bands
+
+| Split | low | medium | high | critical |
+| --- | ---: | ---: | ---: | ---: |
+| train | 18,546 | 222,832 | 206,725 | 60,964 |
+| test | 2,041 | 24,767 | 23,017 | 6,677 |
+
+## Fields
+
+Same as CIRCL, plus:
+
+| Field | Type | Description |
+| --- | --- | --- |
+| `cvss_v3_1_v3_combined` | float | v3.1 score, or v3.0 if v3.1 is missing. Never null in this dataset. |
+| `severity_band` | string | `low`, `medium`, `high`, or `critical`. Never null in this dataset. |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("AgileRLArena/vulnerability-scores-cvss-v3")
+print(ds["train"][0]["description"])
+print(ds["train"][0]["cvss_v3_1_v3_combined"], ds["train"][0]["severity_band"])
+```
+
+Typical training target is `severity_band` (classification) or `cvss_v3_1_v3_combined` (regression). Input is `description`.
+
+## Source and license
+
+Derived from [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores) (CIRCL / Vulnerability-Lookup, [VulnTrain](https://github.com/vulnerability-lookup/VulnTrain)). Paper: [VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification](https://arxiv.org/abs/2507.03607).
+
+License is **CC-BY-4.0**, same as the original. Credit CIRCL when you use this dataset.

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