Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
multi-class-classification
Languages:
Russian
Size:
10K - 100K
License:
metadata
language:
- ru
license: cc-by-4.0
task_categories:
- text-classification
task_ids:
- multi-class-classification
tags:
- russian
- vacancy
- recruitment
- section-classification
- hh.ru
size_categories:
- 10K<n<100K
Vacancy Section Classifier Dataset (RU)
Russian-language dataset for 5-class classification of job vacancy text sections scraped from hh.ru.
Classes
| ID | Label | Description |
|---|---|---|
| 0 | responsibilities | Job responsibilities / duties |
| 1 | requirements | Candidate requirements & skills |
| 2 | terms | Employment terms, salary, benefits |
| 3 | notes | Company self-intro, perks, culture |
| 4 | junk | Headers, boilerplate, noise |
Dataset Construction
- Source: hh.ru job vacancies (2024–2025), crawled and chunked by semantic boundaries
- Splits: train / validation / test (stratified)
- Total rows: ~18 000 (combined_ds union of 9 internal splits)
- Labeling: mix of rule-based pre-labeling + Opus-4 relabeling pass (Tier-2 Opus relabel raised content F1 from 67% → 75%)
- Anonymization: all employer names replaced with industry segment tokens (БАНК, РИТЕЙЛ, ТЕЛЕКОМ, IT-КОМПАНИЯ, ИТ-ИНТЕГРАТОР, ГОССЕКТОР, НЕФТЕГАЗ-ПРОМ, СТРАХОВАНИЕ, ФИНТЕХ-ЛИЗИНГ, ФАРМА-МЕД, ОБРАЗОВАНИЕ, КОМПАНИЯ). Tech-stack tokens (1С, PostgreSQL, Astra Linux, Kaspersky, …) are preserved as label signal.
Usage
from datasets import load_dataset
ds = load_dataset("russian-oracle/vacancy-section-classifier-ru")