README.md

metadata

language:
  - uk
size_categories:
  - 10K<n<100K

WorkUA Resumes Dataset

Dataset Summary

This dataset consists of 104,966 resume entries collected from publicly available pages on Work.ua. Each entry represents structured information extracted from a candidate's resume, including education, work experience, skills, languages, disability status, veteran status, driver license presence, and additional profile metadata.

The dataset is designed for research and development of:

• Resume parsing models
• Information extraction systems
• Vacancy-candidate matching algorithms
• NLP pipelines for Ukrainian-language documents
• Data engineering and ML training workflows
• Career recommendation systems
• Applicant ranking models

All personally identifying information has been removed or anonymized.

Dataset Structure

The dataset is split into three files based on the extraction method and data complexity:

File Descriptions

File	Rows	Description	Extraction Method
resumes_regex.ndjson	84,316	Standard resumes with structured information	Regular expressions
resumes_files_gemini.ndjson	14,397	Resumes uploaded as files (PDF, DOC, etc.)	Gemini 2.5 Flash
resumes_extended_gemini.ndjson	5,253	Resumes with complex additional information	Gemini 2.5 Flash

Total: 104,966 resumes

Data Processing Pipeline

1. Source: Resumes were scraped from Work.ua website where candidates publicly posted their information
2. Format conversion: Original HTML format was transformed into Markdown for easier processing
3. Extraction methods:
   • Regex extraction: Used for standard, well-structured resumes with consistent formatting
   • Gemini 2.5 Flash: Used for file-based uploads and resumes with complex, unstructured additional information that required AI-powered parsing

Schema Overview

All three files share the same schema with 21 fields:

Schema([
    ('id', String),
    ('url', String),
    ('title', String),
    ('candidate_name', String),
    ('age', Int64),
    ('city', String),
    ('desired_salary', Int64),
    ('employment_type', String),
    ('work_location_preference', String),
    ('driver_license', Boolean),
    ('creation_date', Datetime(time_unit='us', time_zone=None)),
    ('other_resumes',
     List(Struct({'title': String, 'url': String, 'resume_id': String, 'description': String}))),
    ('veteran', Boolean),
    ('disability', String),
    ('work_experiences',
     List(Struct({'position': String, 'start_date': String, 'end_date': String, 'company': String, 'city': String, 'industry': String, 'responsibilities': String}))),
    ('recommendations',
     List(Struct({'name': String, 'position': String}))),
    ('languages', List(Struct({'language': String, 'level': String}))),
    ('skills', List(String)),
    ('educations',
     List(Struct({'institution': String, 'faculty': String, 'city': String, 'level': String, 'start_year': Int64, 'end_year': Int64}))),
    ('additional_educations',
     List(Struct({'institution': String, 'start_year': Int64, 'end_year': Int64}))),
    ('additional_info', String)
])

Nested Structure Details

work_experiences

• position: Job title
• start_date: Start date
• end_date: End date
• company: Company name
• city: Work location
• industry: Industry sector
• responsibilities: Job responsibilities description

educations

• institution: Educational institution name
• faculty: Faculty/department name
• city: Institution location
• level: Education level (e.g., "Вища", "Середня спеціальна")
• start_year: Year started
• end_year: Year graduated

additional_educations

• institution: Training institution/course name
• start_year: Year started
• end_year: Year completed

languages

• language: Language name (e.g., "Українська", "English")
• level: Proficiency level (e.g., "вільно", "базовий", "intermediate")

recommendations

• name: Recommender's name
• position: Recommender's position/title

other_resumes

• title: Title of other resume by same candidate
• url: URL to the other resume
• resume_id: ID of the other resume
• description: Brief description

Data Example

{
  "id": "123456",
  "url": "https://www.work.ua/resumes/123456/",
  "title": "Будівельник",
  "candidate_name": "Іван",
  "age": 32,
  "city": "Київ",
  "desired_salary": 25000,
  "employment_type": "повна",
  "work_location_preference": "офіс",
  "driver_license": true,
  "creation_date": "2025-03-10T12:30:00",
  "veteran": false,
  "disability": null,
  "skills": ["Штукатурка", "Монтаж гіпсокартону"],
  "languages": [{"language": "Українська", "level": "вільно"}],
  "work_experiences": [
    {
      "position": "Будівельник",
      "start_date": "2020-01",
      "end_date": "present",
      "company": "БудКомпанія",
      "city": "Київ",
      "industry": "Будівництво",
      "responsibilities": "Виконання будівельних робіт"
    }
  ],
  "educations": [
    {
      "institution": "КНУБА",
      "faculty": "Промислове та цивільне будівництво",
      "city": "Київ",
      "level": "Вища",
      "start_year": 2012,
      "end_year": 2016
    }
  ],
  "additional_educations": [],
  "recommendations": [],
  "other_resumes": [],
  "additional_info": "Готовий до відряджень."
}

Loading the Dataset

Using Polars

import polars as pl

# Load individual files
resumes_regex = pl.read_ndjson("resumes_regex.ndjson")
resumes_files = pl.read_ndjson("resumes_files_gemini.ndjson")
resumes_extended = pl.read_ndjson("resumes_extended_gemini.ndjson")

# Combine all resumes
all_resumes = pl.concat([resumes_regex, resumes_files, resumes_extended])

print(f"Total resumes: {len(all_resumes)}")

Using Pandas

import pandas as pd

# Load individual files
resumes_regex = pd.read_json("resumes_regex.ndjson", lines=True)
resumes_files = pd.read_json("resumes_files_gemini.ndjson", lines=True)
resumes_extended = pd.read_json("resumes_extended_gemini.ndjson", lines=True)

# Combine all resumes
all_resumes = pd.concat([resumes_regex, resumes_files, resumes_extended], ignore_index=True)

print(f"Total resumes: {len(all_resumes)}")

Intended Use

• Resume parsing: Train and evaluate resume parsing models
• Information extraction: Develop IE systems for structured data extraction
• Semantic search: Build search engines for candidate discovery
• Text classification: Classify resumes by industry, job type, or skill level
• Matching algorithms: Develop vacancy-candidate matching systems
• NLP research: Study Ukrainian-language document processing
• Career analytics: Analyze job market trends and skill requirements
• Recommendation systems: Build career path and job recommendation engines

Limitations

• Some fields may be incomplete due to original document variability
• Date formats may vary across entries (especially in work_experiences)
• The quality of extraction may vary between regex-based and Gemini-based processing
• Some additional_info fields may contain unstructured or noisy data
• Language proficiency levels use non-standardized terms
• Salary information may be missing for many entries

Data Quality Notes

Extraction Method Comparison

• Regex extraction (resumes_regex.ndjson):

  • Higher consistency for well-structured fields
  • May miss information in non-standard formats

• Gemini extraction (resumes_files_gemini.ndjson, resumes_extended_gemini.ndjson):

  • Better handling of complex, unstructured information
  • May introduce parsing variations
  • Can extract information from diverse file formats

Ethical Considerations

• All resumes were collected from publicly available pages on Work.ua
• Personally identifying information has been removed or anonymized
• Contact information (phone numbers, emails) is not included
• The dataset should be used for research and development purposes only
• Users should respect privacy and not attempt to re-identify individuals

Language

The dataset is primarily in Ukrainian, with some entries containing information in Russian or other languages.

Acknowledgments

• Data source: Work.ua
• Extraction tools: Google Gemini 2.5 Flash for AI-powered parsing
• Processing framework: Polars for efficient data handling