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Divyanandh/resume-matching-dataset-v2

πŸ“„ Dataset Card - Resume Matching Dataset v2 Overview This dataset is designed for training and evaluating large language models (LLMs) on resume-job matching tasks, specifically in AI and software engineering domains. All data samples were generated using GPT-4o-mini. The dataset exclusively contains synthetic data β€” no real resumes, self-introductions, or job postings are used. This dataset targets three roles: AI/LLM Developer Frontend Developer Backend… See the full description on the dataset page: https://huggingface.co/datasets/Divyanandh/resume-matching-dataset-v2.

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Dataset Card

πŸ“„ Dataset Card - Resume Matching Dataset v2

Overview

This dataset is designed for training and evaluating large language models (LLMs) on resume-job matching tasks, specifically in AI and software engineering domains.

  • β€”All data samples were generated using GPT-4o-mini.
  • β€”The dataset exclusively contains synthetic data β€” no real resumes, self-introductions, or job postings are used.
  • β€”This dataset targets three roles:
  • β€”AI/LLM Developer
  • β€”Frontend Developer
  • β€”Backend Developer

Each sample includes:

  • β€”A job post (jobpost)
  • β€”A resume (resume)
  • β€”A self-introduction (selfintro)
  • β€”Grade evaluations (resume_grade, selfintro_grade)
  • β€”Matching evaluation (evaluation) with total and component scores

πŸ“‚ Structure

FieldDescription
jobpostSynthetic job posting text
resume_gradeResume quality grade (상/쀑/ν•˜)
selfintro_gradeSelf-introduction quality grade (상/쀑/ν•˜)
resumeSynthetic resume text
selfintroSynthetic self-introduction text
evaluationGPT-generated evaluation including feedback and reasoning
total_scoreTotal matching score (0–100)
resume_scoreResume-only matching score (0–50)
selfintro_scoreSelf-intro-only matching score (0–50)

πŸ§ͺ Example

json
{
  "jobpost": "React.js 기반 μ›Ή ν”„λ‘ νŠΈμ—”λ“œ 개발자λ₯Ό λͺ¨μ§‘ν•©λ‹ˆλ‹€. 2λ…„ μ΄μƒμ˜ κ²½λ ₯ ν•„μˆ˜...",
  "resume_grade": "상",
  "selfintro_grade": "쀑",
  "resume": "홍길동은 3λ…„κ°„ ν”„λ‘ νŠΈμ—”λ“œ 개발 κ²½ν—˜μ„ λ³΄μœ ν•˜κ³  있으며...",
  "selfintro": "μ €λŠ” μ‚¬μš©μž κ²½ν—˜μ„ μ΅œμš°μ„ μœΌλ‘œ κ³ λ €ν•˜λŠ” ν”„λ‘ νŠΈμ—”λ“œ κ°œλ°œμžμž…λ‹ˆλ‹€...",
  "evaluation": "이λ ₯μ„œμ™€ μžκΈ°μ†Œκ°œμ„œλŠ” 직무 μš”κ±΄κ³Ό λŒ€μ²΄λ‘œ λΆ€ν•©ν•©λ‹ˆλ‹€. 총점: 88/100. 강점: React ν”„λ‘œμ νŠΈ κ²½ν—˜, νŒ€ ν”„λ‘œμ νŠΈ ν˜‘μ—… κ²½ν—˜. 보완점: Next.js κ²½ν—˜ 미기재.",
  "total_score": 88,
  "resume_score": 45,
  "selfintro_score": 43
}

πŸ“Š Dataset Statistics

  • β€”Total Samples: 74,147
  • β€”Target Roles: AI/LLM Developer, Frontend Developer, Backend Developer
  • β€”Data Source: Fully synthetic (GPT-4o-mini generated)
  • β€”Evaluation: GPT-4o-mini scoring and reasoning
  • β€”Language: Korean

⚠️ Limitations

  • β€”All data is synthetically generated and may not fully capture the nuances of real-world hiring processes.
  • β€”This dataset is intended for research and educational purposes only. It should not be used for real-world hiring or personnel decisions.

πŸ—οΈ Dataset Creation Notes

  • β€”Developed as part of a team project focusing on AI-based career matching systems.
  • β€”Data generation, grading, and evaluation were fully automated using GPT-4o-mini.
  • β€”No personally identifiable information (PII) or copyrighted material is included.

πŸ”— Citation If you use this dataset, please cite:

  @misc{resume-matching-dataset-v2,
    title={Resume Matching Dataset v2},
    author={Rezoom Team},
    year={2025},
    howpublished={\url{https://huggingface.co/datasets/jminc/resume-matching-dataset-v2}}
  }