README.md

---
language: en
pretty_name: Job Training Data for JSON Extraction
task_categories:
- text-generation
tags:
- json
- web-scraping
- job-postings
- training-data
- json-extraction
size_categories: medium
---

# Job Training Data

Training dataset for fine-tuning LLMs to extract structured JSON from job postings.

## Description

This dataset contains **12,000 examples** of job postings in markdown format paired with their JSON extractions. Used to train the `job-posting-extractor-qwen` model.

## Data Format

Each example contains:

- `instruction`: What to do (e.g., "Extract job fields as JSON").

- `input`: Job posting in markdown format.

- `output`: Expected JSON output (as a string).

### Example Entry

```json
{
  "instruction": "Extract all job fields as JSON object.",
  "input": "# Job Position\n**Position:** Platform Engineer\n**Company:** DATAECONOMY\n**Location:** Charlotte, NC\n\n## Job Description\nRole: Platform engineer...",
  "output": "{\"job_title\": \"Platform Engineer\", \"company\": \"DATAECONOMY\", \"location\": \"Charlotte, NC\"}"
}
```

## How It Was Created

1. Data sourced from webscraped job postings.

2. Converted to markdown using template-based generation.

3. JSON labels programmatically extracted from the scraped data.

4. Augmented with 15 instruction variations.

## Dataset Statistics

| Metric | Value |
|--------|-------|
| Total examples | 12,000 |
| Unique JSON fields | 7 (job_title, company, location, work_type, description, experience_level, salary) |
| Instruction variations | 15 |

## Files

- `job_training_data.json`: Main training data (12,000 examples).

## License & Attribution

This dataset is licensed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

You are free to **use, share, copy, modify, and redistribute** this material for any purpose (including commercial use), **provided that proper attribution is given**.

### Attribution requirements

Any reuse, redistribution, or derivative work **must** include:

1. **The creator's name**: `HelixCipher`

2. **A link to the original repository**:  

   https://github.com/HelixCipher/fine-tuning-an-local-llm-for-web-scraping

3. **An indication of whether changes were made**

4. **A reference to the license (CC BY 4.0)**

#### Example Attribution

> This work is based on *Fine-Tuning An Local LLM for Web Scraping* by `HelixCipher`.  
> Original source: https://github.com/HelixCipher/fine-tuning-an-local-llm-for-web-scraping

> Licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0).

You may place this attribution in a README, documentation, credits section, or other visible location appropriate to the medium.

Full license text: https://creativecommons.org/licenses/by/4.0/