Datasets:

Azzindani
/

ID_REG_QA_Small

This repository contains a question-answer (QA) dataset generated from parsed Indonesian regulations, focusing on legal quoting and comprehension. Designed to facilitate legal-aware LLMs, the dataset provides direct QA mappings to individual articles for contextual understanding and reference.

📌 Dataset Highlights

Source: Generated from the ID_REG_Parsed repository
Format: QA pairs based on individual articles (no chunking)
Scale: Augmented by applying 10 QA templates across suitable regulation entries
Filtering: Programmatic filtering removes redundant or overly broad article explanations
Target Use: Train/test LLMs for regulation comprehension, legal quoting, and document-level QA

⚙️ Pipeline Overview

Environment: Executed in a single Jupyter Notebook on Kaggle Cloud
Data Flow:
1. Pull parsed articles from ID_REG_Parsed
2. Filter and refine results for clarity and legal context
3. Apply template-driven QA generation (10 variations)
4. Push QA dataset directly to this repository
Performance:
- Completed in ~20 minutes using Kaggle GPU resources
- Cloud-to-cloud transfer without local storage dependency

🧠 Use Cases

Fine-tuning LLMs for legal question answering
Benchmarks for article referencing and quoting
Few-shot prompting for legal search assistants
Legal text evaluation with grounded answers

⚠️ Disclaimer

This dataset is intended for research and development only. QA pairs are generated synthetically from publicly available legal text and may not reflect official interpretations.

🙏 Acknowledgments

Hugging Face for hosting open datasets
Kaggle for compute and cloud-to-cloud capabilities