Datasets:
license: mit
language:
- en
task_categories:
- text-classification
tags:
- resume
- cv
- skills
- hr-tech
- nli
- synthetic
pretty_name: SkillJev data
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
- split: heldout_locked
path: heldout_locked.jsonl
SkillJev data
This dataset holds (job requirement, candidate evidence) pairs with three labels: skill group, evidence (supported/partial/contradicted/insufficient), and proficiency (0–5 or unknown; unknown ≠ 0). It was used to train niko0xdev/skilljev.
Files
| File | Rows | Description |
|---|---|---|
train.jsonl |
38,938 | training set (v008, after PII filtering) |
heldout_locked.jsonl |
90 | locked evaluation set, byte-identical to the version used for every reported metric (heldout_locked_v003.sha256) |
trainer_train.jsonl, trainer_heldout_locked.jsonl |
116,814 / 270 | same data in multiple-choice trainer format {state, question, options, type, target, meta}, 3 rows per sample (one per head) |
RUBRIC.md, SCHEMA.md, TAXONOMY.md |
– | labeling rubric (incl. Real-CV convention v0.2), JSON schema, skill taxonomy (55 skills, 10 groups) |
Composition (train)
Source (meta.source) |
Rows | Labels (meta.label_status) |
|---|---|---|
synthetic (synthetic_v002/v004/v005/v006/v008) |
31,960 | template-generated: case types × rubric anchors × paraphrases, including level 3/4/5 boundary, partial-vs-supported contrast, and convention-v0.2 cases |
djinni_cv_en real CV excerpts |
6,928 | 1,223 agent_reviewed_v005 (read individually and relabelled per rubric); 5,705 silver_rules_v006 (rule-based labels, about 76% agreement with reviewed labels); convention v0.2 applied to all |
handwritten_v001 |
50 | hand-written draft labels |
Held-out: 70 synthetic samples use templates that never appear in train; 20 are reviewed real excerpts. The CV records they come from are excluded from train, and train is deduplicated against held-out text.
Sources & attribution
Real excerpts are single sentences taken from lang-uk/recruitment-dataset-candidate-profiles-english (Djinni candidate profiles, MIT license). Please cite that dataset as well when using these excerpts. Synthetic text was written from templates for this project.
Anonymization
- Only short excerpts are kept (≤ ~260 characters, one sentence or line). No full profiles and no source profile IDs beyond the dataset's own record id (
meta.source_record). - Excerpts were filtered at extraction and rescanned before publishing. Any row matching an email, phone number, URL or domain, social handle (@, LinkedIn, GitHub, Telegram, Skype), self-introduction ("my name is"), or a list of common first names was dropped (103 rows removed from train, 0 from held-out).
- Residual risk: company or project names can remain, and name detection is heuristic. Report issues via the Community tab and the rows will be removed.
Labels
See RUBRIC.md. Labels are synthetic or agent/rule-produced and have not been verified by a professional recruiter. Use this data for research and prototyping, not as ground truth for hiring decisions.