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--- |
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pretty_name: EPDK Corpus (v2) |
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tags: |
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- turkish |
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- energy-market |
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- legislation |
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- regulations |
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- epdk |
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- ocr |
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- qwen-vl |
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- text-extraction |
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- domain-specific |
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- language-modeling |
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- legal |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: text |
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dtype: string |
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- name: kind |
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dtype: string |
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- name: method |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 3577 |
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download_size: 72000000 |
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dataset_size: 145000000 |
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configs: |
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- config_name: v2 |
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data_files: |
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- split: train |
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path: epdk_corpus_dataset.jsonl |
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language: |
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- tr |
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license: apache-2.0 |
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task_categories: |
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- text-generation |
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size_categories: |
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- 1K<n<10K |
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version: 2.0.0 |
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--- |
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# Dataset Card for **EPDK Electricity Market Corpus (v2)** |
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## Dataset Summary |
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This corpus contains high-quality Turkish text extracted from **documents published by the Turkish Energy Market Regulatory Authority (EPDK – Enerji Piyasası Düzenleme Kurumu)**. |
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The source materials include regulations, board decisions, and official communiqués related to the **electricity market legislation**. |
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Version 2.0.0 introduces a **complete re-extraction of the text using the Qwen-2.5-VL vision-language model**, which achieves dramatically better OCR accuracy than Tesseract—particularly on scanned and complex multi-column PDFs. |
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The result is a significantly cleaner and more semantically faithful representation of the original legal texts. |
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--- |
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## Version History |
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| Version | Description | Date | Notes | |
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|----------|--------------|------|-------| |
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| **v1.0.0** | Initial release using Tesseract OCR (≈ 3.4 K docs) | 2025-04 | Baseline version. Stored under `/v1.0.0/` branch. | |
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| **v2.0.0** | Re-extracted with Qwen-2.5-VL OCR (≈ 3.6 K docs), normalized fields (`id`, `text`, `kind`, `method`) | 2025-10 | Recommended for downstream use. | |
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To load a specific version: |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("ogulcanakca/epdk_corpus", revision="v2.0.0") |
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``` |
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--- |
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## Intended Uses |
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Designed primarily for **language-model domain adaptation** and **continued pre-training** on Turkish regulatory language. |
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Potential downstream tasks include: |
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* Legal Question Answering over EPDK documents |
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* Summarization of laws and regulations |
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* Document classification or clustering (e.g., tebliğ / karar / yönetmelik) |
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* Information retrieval and RAG training for energy-sector LLMs |
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--- |
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## Languages |
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All text is in **Turkish (`tr`)**. |
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--- |
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## Data Fields |
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| Field | Type | Description | |
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| -------- | ------ | ---------------------------------------------------------------- | |
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| `id` | string | Unique SHA-1-based identifier for the document | |
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| `text` | string | Cleaned text extracted from the source file | |
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| `kind` | string | File type (`docx`, `pdf`, `xls`, etc.) | |
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| `method` | string | Extraction method (`python-docx`, `pandas`, `qwen-vl-ocr`, etc.) | |
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--- |
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## Example Instance |
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```json |
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{ |
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"id": "b3f7a6d9c49e", |
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"text": "Enerji Piyasası Düzenleme Kurumundan:\nKURUL KARARI\nKarar No: 9284 Karar Tarihi: 02/04/2020\n...", |
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"kind": "docx", |
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"method": "python-docx" |
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} |
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``` |
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--- |
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## Data Structure & Access |
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* Format: `JSONL` (one document per line) |
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* Total examples: ≈ 3 577 |
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* Dataset path: `v2/final_corpus_dataset.jsonl` |
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--- |
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```bibtex |
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@dataset{ogulcanakca_epdk_corpus_v2, |
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author = {Oğulcan Akca}, |
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title = {EPDK Corpus (v2)}, |
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year = {2025}, |
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url = {https://huggingface.co/datasets/ogulcanakca/epdk_corpus} |
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} |
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``` |
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--- |
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[akca_ogulcan@hotmail.com](mailto:akca_ogulcan@hotmail.com) |
