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ai-colombia/job-searcher-data

AI Job Searcher Training Data (V3) Fine-tuning dataset for a career advisor AI specializing in Nordic and European job markets. V3 (March 2026): Added 150 freeform/narrative-style Analyze examples modeled after real job postings from finn.no and arbeidsplassen.nav.no. Now includes startup-style, agency, generalist, and narrative formats alongside V2 rigid-template examples. Dataset Description This dataset contains 1,190 training examples across 9 languages and… See the full description on the dataset page: https://huggingface.co/datasets/ai-colombia/job-searcher-data.

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face
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AI Job Searcher Training Data (V3)

Fine-tuning dataset for a career advisor AI specializing in Nordic and European job markets.

V3 (March 2026): Added 150 freeform/narrative-style Analyze examples modeled after real job postings from finn.no and arbeidsplassen.nav.no. Now includes startup-style, agency, generalist, and narrative formats alongside V2 rigid-template examples.

Dataset Description

This dataset contains 1,190 training examples across 9 languages and 5 task categories, formatted as chat conversations (system/user/assistant) suitable for fine-tuning LLMs.

Task Categories

CategoryExamplesDescription
Cover Letter Generation208Professional cover letters from job description + user profile
Job Listing Analysis (V2+V3)~450Structured analysis of full-length job postings: 300 rigid-template (V2) + 150 freeform/narrative (V3)
Interview Preparation2087-8 interview questions with STAR-format behavioral + technical answers
Job Search Optimization208Optimized search keywords for Nordic job portals
CV Bullet Points208Polished, achievement-oriented bullet points from raw descriptions

Languages

LanguageCodeExamples
Englishen~195
Norwegianno~192
Swedishsv~149
Danishda~141
Germande~133
Finnishfi~125
Frenchfr~125
Spanishes~115
Portuguesept~107

Industries Covered

IT/Technology, Healthcare, Engineering, Finance, Education, Energy, Marketing, UX Design, Project Management, Proptech, Cleantech, Fintech, AI/ML, Consulting

V3 Analyze Improvements (on top of V2)

  • —150 freeform/narrative-style postings modeled after real finn.no + arbeidsplassen.nav.no listings
  • —Startup-style (storytelling intro, equity, culture-forward, "About us / About you / What we offer")
  • —Finn.no-style (Norwegian marketing copy: "Hva du vil jobbe med", "Vi ser etter deg som", "Hva vi tilbyr")
  • —Recruitment agency (third-person: "On behalf of our client...")
  • —Narrative/paragraph (no rigid sections, flowing paragraphs)
  • —Real Nordic companies (Vander, Chargitect, Cognite, Klarna, Northvolt, Wolt, etc.)
  • —Mixed employment types (60-100%, flexible)

V2 Analyze Features (retained)

  • —Full-length job descriptions (800-1,700 chars vs. old 200-340 chars)
  • —Structured sections per country (e.g. NO: Arbeidsoppgaver / Kvalifikasjoner / Vi tilbyr)
  • —13 job profiles across 10+ industries
  • —Varied match scores (20-95%, not clustered at 60-75%)
  • —Role levels: Junior 20%, Mid 40%, Senior 30%, Lead 10%

Format

Each example follows the OpenAI/HuggingFace chat format:

json
{
  "messages": [
    {"role": "system", "content": "You are a professional career advisor..."},
    {"role": "user", "content": "Analyze this job posting:\n\nEquinor — Programvareutvikler\nOslo\n\nEquinor er en ledende aktør innen IT/teknologi..."},
    {"role": "assistant", "content": "**Påkrevde ferdigheter:** Python, Docker...\n**Match-score:** 72%"}
  ]
}

System Prompt

You are a professional career advisor specializing in Nordic and European job markets. You help job seekers with cover letters, job analysis, interview preparation, and career guidance. Respond in the same language as the user's query.

Files

  • —train_combined.jsonl — All 1,190 examples combined (shuffled)
  • —train_{lang}.jsonl — Per-language splits (no, sv, da, en, es, pt, fr, de, fi)

Usage

python
from datasets import load_dataset

# Load all data
dataset = load_dataset("ai-colombia/job-searcher-data", data_files="train_combined.jsonl")

# Load specific language
dataset_no = load_dataset("ai-colombia/job-searcher-data", data_files="train_no.jsonl")

Realistic Nordic Content

Includes authentic company names (Equinor, Spotify, Novo Nordisk, Nokia, Wolt, Klarna, Chargitect, Vander, Cognite, Northvolt, etc.), job titles, cities, salary ranges, and job portals across Nordic and European countries.

License

Apache 2.0