README.md

---
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:

```python
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

```json
{
  "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`

---

```bibtex
@dataset{ogulcanakca_epdk_corpus_v2,
  author    = {Oğulcan Akca},
  title     = {EPDK Corpus (v2)},
  year      = {2025},
  url       = {https://huggingface.co/datasets/ogulcanakca/epdk_corpus}
}
```

---

[akca_ogulcan@hotmail.com](mailto:akca_ogulcan@hotmail.com)