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README.md
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license: apache-2.0
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[](https://github.com/azzindani)
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##
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* **
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* **Cloud Pipeline**: Scraped using **6 Google Colab nodes**, pushed directly to Hugging Face
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* **Duration**: \~200 hours distributed scraping in total
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* **Failures**: Some links unreachable or invalid
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## 🧠
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## ⚠️ Disclaimer
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This dataset is
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No warranties are made regarding the accuracy, completeness, or legal validity of the content.
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## 🙏 Acknowledgments
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license: apache-2.0
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# 🇮🇩 Indonesian Regulation Parsed Dataset
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This dataset contains the **parsed content of over 250,000 Indonesian regulations**, extracted directly from PDFs at the article level — **without chunking** — resulting in **3.5+ million rows** representing individual articles and clauses.
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## 📄 Dataset Overview
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* **Source**: Parsed from the original PDF collection of Indonesian regulations (\~250K documents)
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* **Parsed Units**: Articles, clauses, or sections as atomic units (no chunking applied)
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* **Size**: Over **3.5 million rows** of parsed text data
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* **Token Count**: Approximately **2 billion tokens** in total
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## ⚙️ Processing Pipeline
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* **Data Flow**:
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1. Pull PDFs from the **[ID\_REG repository](https://huggingface.co/datasets/Azzindani/ID_REG)**
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2. Parse and extract articles using automated PDF text extraction methods
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3. Push cleaned, parsed text to this Hugging Face repository
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* **Infrastructure**:
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* Utilized **6 simultaneous Google Colab instances** for parallel processing
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* Cloud-to-cloud architecture enables direct transfer without local storage bottlenecks
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* Total processing time: \~30 hours
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## 🧠 Use Cases
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* Fine-grained legal text analysis and search
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* Article-level classification, summarization, or information retrieval
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* Training large language models on legal text with natural article boundaries
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* Developing Indonesian regulation compliance tools
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## ⚠️ Disclaimer
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This dataset is provided **exclusively for research and development** purposes. It contains parsed content derived from publicly available legal PDFs, and no guarantees are made regarding completeness or legal accuracy.
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## 🙏 Acknowledgments
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* Hosted on **[Hugging Face](https://huggingface.co/)** — a wonderful platform for open data sharing
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* Processing powered by **[Google Colab](https://colab.research.google.com/)** — scalable cloud resources that made fast parallel parsing possible
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