Dataset Card for resume-json-extraction-5k

Dataset Description

This dataset contains 4,879 resume examples formatted for fine-tuning language models to extract structured JSON information from resume text.

Dataset Summary

The dataset consists of resume text paired with structured JSON outputs containing:

• Job titles (current and previous)
• Companies (current and previous)
• Years of experience
• Seniority level
• Primary domain and industries
• Core and secondary skills
• Tools used
• Leadership experience
• Key achievements
• Location
• Professional summary

This dataset is designed for supervised fine-tuning of language models to perform information extraction tasks. Each training example consists of:

• Input: Raw resume text (unstructured)
• Output: Structured JSON representation with extracted professional information

The dataset consists of resume text paired with structured JSON outputs containing comprehensive professional profile information extracted from resumes.

What This Dataset Contains

Each example in this dataset represents a complete conversation formatted for training:

1. System Message: Instructions for the model to act as an expert resume parser
2. User Message: The raw resume text to be parsed
3. Assistant Message: The structured JSON output with extracted information

The data is formatted using Qwen3's chat template (<|im_start|> and <|im_end|> tokens) and is ready for direct use in fine-tuning pipelines.

Languages

The dataset is in English.

Dataset Structure

Data Instances

Each instance contains a single field text with the complete chat template formatted conversation:

{
  "text": "<|im_start|>system\nYou are an expert resume parser. Extract structured information from resumes and return ONLY valid JSON. Do not include explanations or extra text.<|im_end|>\n<|im_start|>user\nResume:\n[Raw resume text here...]\n<|im_end|>\n<|im_start|>assistant\n{\"current_title\": \"...\", \"previous_titles\": [...], ...}<|im_end|>"
}

Example Data Structure

Here's what a complete example looks like:

Input (User Message):

Resume:
SOFTWARE ENGINEER
John Doe
Email: john@example.com

Summary:
Experienced software engineer with 5 years in Python and web development.

Experience:
Senior Software Engineer | Tech Corp | 2020 - Present
- Developed REST APIs using Python and Flask
- Led team of 3 junior developers
...

Output (Assistant Message - Structured JSON):

{
  "current_title": "Senior Software Engineer",
  "previous_titles": ["Software Engineer"],
  "current_company": "Tech Corp",
  "previous_companies": ["Previous Company"],
  "years_experience": 5,
  "seniority": "senior",
  "primary_domain": "Software Engineering",
  "industries": ["Technology", "SaaS"],
  "core_skills": ["Python", "REST APIs", "Web Development"],
  "secondary_skills": ["Team Leadership", "Flask", "API Design"],
  "tools": ["Python", "Flask", "Git"],
  "leadership_experience": true,
  "key_achievements": [
    "Led team of 3 junior developers",
    "Developed REST APIs serving 1M+ requests/day"
  ],
  "location": null,
  "summary": "Experienced software engineer with 5 years in Python and web development; led teams and built scalable REST APIs."
}

Data Fields

• text: Complete chat template formatted text with system prompt, user resume, and assistant JSON response

Structured JSON Schema

The assistant output in each example contains a structured JSON object with the following fields:

Core Profile Fields

• current_title (string): The most recent or current job title
• previous_titles (array of strings): List of prior job titles (up to 3 most recent)
• current_company (string): The most recent or current company name
• previous_companies (array of strings): List of prior companies (up to 3 most recent)
• years_experience (number): Total years of professional experience (e.g., 7.5)
• seniority (string): Seniority level - one of: "junior", "mid", "senior", "lead", or "principal"

Domain and Industry

• primary_domain (string): Primary professional domain or field (e.g., "Software Engineering", "Marketing")
• industries (array of strings): List of industries the person has worked in (up to 3 most prominent)

Skills and Tools

• core_skills (array of strings): Primary/core skills (up to 3 most prominent)
• secondary_skills (array of strings): Secondary or supporting skills (up to 3 most prominent)
• tools (array of strings or null): Tools, software, or technologies used (up to 3 most prominent)

Experience and Achievements

• leadership_experience (boolean): Whether the resume indicates leadership, management, or mentoring experience
• key_achievements (array of strings): Impact-oriented achievements with metrics or outcomes (up to 3 highest-impact)

Additional Information

• location (string or null): Geographic location if mentioned in the resume
• summary (string): Concise professional summary (maximum 300 characters)

Field Constraints

• Array fields are limited to a maximum of 3 items (most recent or most relevant)
• Fields may be null if information is not present or cannot be confidently determined
• All information is extracted only from what is explicitly stated in the resume (no inference)

Data Splits

• train: 4,879 examples (100% of dataset)

Dataset Creation

Source Data

The dataset was created from extracted resume data where:

1. Raw resume text was collected
2. Structured information was extracted using LLM-based extraction
3. Examples were formatted using Qwen3 chat template
4. Only successful extractions (no errors) were included

Preprocessing

1. Filtered out examples with extraction errors
2. Converted to Qwen3 chat template format
3. Applied tokenizer chat template with:
   • add_generation_prompt=False (for training)
   • enable_thinking=False (for JSON output)

Annotations

Each example includes:

• System prompt: Instructions for resume parsing
• User input: Raw resume text
• Assistant output: Structured JSON with extracted information

Uses

Direct Use

This dataset is intended for fine-tuning language models to:

• Extract structured information from resumes
• Generate JSON output from unstructured text
• Perform information extraction tasks

Out-of-Scope Use

This dataset should not be used for:

• Training models to generate fake resumes
• Extracting personal information for unauthorized purposes
• Any use case that violates privacy or data protection laws

Bias, Risks, and Limitations

Bias

• Dataset may reflect biases present in the source resume data
• Geographic and industry representation may be uneven
• May not represent all resume formats or styles

Risks

• Models trained on this data may perpetuate biases
• Extracted information should be verified for accuracy
• Not suitable for automated hiring decisions without human review

Limitations

• Limited to ~5k examples
• May not generalize to all resume formats
• JSON structure is fixed and may not capture all resume information

Citation

@misc{resume_json_extraction_5k,
  title={resume-json-extraction-5k},
  author={Sandeep Panem},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/sandeeppanem/resume-json-extraction-5k}}
}

License

This dataset is licensed under Apache 2.0.

Contact

For questions or issues, please open an issue on the dataset repository.