sallani/matchcv-synthetic-dataset

MatchCV Synthetic Training Dataset

500 synthetic, anonymous CV/Job matching pairs — generated algorithmically, zero real personal data.

Used to fine-tune `sallani/MatchCV-Qwen2.5-0.5B`.

What's inside

Each record is an instruction-following example:

{
  "instruction": "Analyse le profil candidat...",
  "input": "=== CV ANONYMISÉ ===\n...\n=== OFFRE ===\n...",
  "output": "Score de matching : 73.5/100\nCompétences correspondantes : ...\nGaps techniques : ...\nRecommandation : ..."
}

CV fields (all synthetic, no real person)

• —Formation — education level (Bac+2 to Bac+8, Bootcamp)
• —Expérience — years + seniority (junior / confirmé / senior / expert)
• —Compétences — technical skills (Python, Docker, AWS, ISO 27001, LoRA, etc.)
• —Mobilité — preferred work mode (Remote, Hybrid, On-site)
• —Disponibilité — availability (Immediate, 1 month, 3 months)

Job description fields

• —Type — mission type (Architecture, MLOps, Audit, DevSecOps, etc.)
• —Secteur — industry sector (Fintech, Cybersécurité, IA générative, etc.)
• —Durée — contract duration
• —Mode — work mode
• —Expérience requise — required years range
• —Compétences requises / souhaitées — required / nice-to-have skills

Output fields

• —Score de matching — 0 to 100
• —Compétences correspondantes — matched skills
• —Gaps techniques — missing required skills
• —Compétences bonus — nice-to-have skills the candidate has
• —Recommandation — hiring recommendation

Dataset stats

Privacy

All data is algorithmically generated from a vocabulary of technical skills, sectors, and education levels. No names, emails, phone numbers, addresses or any identifier. Safe to use for model training without GDPR constraints.

License

Apache 2.0