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