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README.md

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+---
+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/

job_training_data.json

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+size 22364609