horelulus/ID_REG_QA_Small

--- license: apache-2.0 --- # 🧾 Indonesian Legal QA Dataset 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](https://huggingface.co/datasets/Azzindani/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](https://huggingface.co/)** for hosting open datasets * **[Kaggle](https://www.kaggle.com/)** for compute and cloud-to-cloud capabilities ---license: apache-2.0 # 🧾 印尼法律问答数据集（Indonesian Legal QA Dataset） 本仓库包含一个**问答（QA, Question-Answering）数据集**，该数据集源自已解析的印尼法律法规文本，聚焦**法律引用与文本理解**任务。本数据集旨在助力具备法律认知能力的大语言模型（LLM, Large Language Model）开发，提供了与单条法律条文一一对应的问答映射，以支持上下文理解与参考引用。 --- ## 📌 数据集核心亮点 * **数据来源**：源自[ID_REG_Parsed](https://huggingface.co/datasets/Azzindani/ID_REG_Parsed)数据集仓库 * **数据格式**：基于单条法律条文生成的问答对（未进行文本分块处理） * **数据规模**：通过在适配的法规条目上应用10种问答模板完成数据增强 * **筛选机制**：通过程序化过滤移除冗余或过于宽泛的条文解释内容 * **目标用途**：用于训练/测试大语言模型的**法规文本理解**、**法律引用**以及**文档级问答**能力 --- ## ⚙️ 数据处理流程总览 * **运行环境**：在Kaggle云平台的单份Jupyter Notebook中执行 * **数据流转流程**： 1. 从`ID_REG_Parsed`数据集拉取已解析的法律条文 2. 对结果进行筛选与优化，确保内容清晰且符合法律语境 3. 基于模板驱动的问答生成方法（共10种变体）生成问答对 4. 将生成的问答数据集直接推送至本仓库 * **运行性能**：使用Kaggle GPU资源完成全部流程耗时约20分钟，采用云到云的传输方式，无需依赖本地存储 --- ## 🧠 典型应用场景 * 针对法律问答任务的大语言模型微调 * 用于**条文引用与法律引用**能力的基准测试 * 面向法律检索助手的少样本（Few-shot）提示工程 * 基于标准答案的法律文本效果评估 --- ## ⚠️ 免责声明 本数据集仅用于**研究与开发**场景。问答对均由公开可用的法律文本合成生成，不代表官方法律解释。 --- ## 🙏 致谢 * **[Hugging Face](https://huggingface.co/)** 提供开源数据集托管服务 * **[Kaggle](https://www.kaggle.com/)** 提供计算资源与云到云数据传输能力