README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
- fill-mask
language:
- de
tags:
- wikipedia
- german
- cleaned
- nlp
- apex-datasets
size_categories:
- 10k<n<100k
---

<h1 align="center">💎 APEX DATASETS</h1>
<h3 align="center">"Believe in Apex. Become what's Next."</h3>

<br>

<div align="center">

<h1 align="center">🇩🇪 German Wikipedia Cleaned: Platinum Sample</h1>

[![License](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-red.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[![Language](https://img.shields.io/badge/Language-German%20(Deutsch)-yellow.svg)]()
[![Source](https://img.shields.io/badge/Source-Wikipedia-lightgrey.svg)]()
[![Status](https://img.shields.io/badge/Status-Premium%20Sample-success.svg)]()

**Cleaned • Deduplicated • Optimized for LLMs**

</div>

---

## 💎 The "Art" of Clean Data
This is not just a dataset; it's a **masterpiece of data engineering**. We have processed raw German Wikipedia dumps, removing all HTML tags, boilerplate, and noise, leaving only the **purest linguistic structures** for your AI training needs.

> *"Quality over Quantity. But we give you both."* — **Apex Datasets**

---

## 🏗️ Dataset Structure (Sample)

We provide a **ready-to-use** split, formatted in **Parquet** for maximum efficiency.

| File Name | Rows | Usage Scenario | Description |
| :--- | :---: | :--- | :--- |
| **`sample_train.parquet`** | **400** | 🏋️ Fine-Tuning | Rich, diverse sentences for teaching the model context. |
| **`sample_test.parquet`** | **100** | 🧪 Evaluation | Unseen data for testing model performance (perplexity). |
| **`dataset_statistics.json`** | - | 📊 Analysis | Complete metadata and statistical breakdown. |

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## 📊 Quick Stats (Full Version)
* **Total Size:** 8.21GB+
* **Sentences:** 3,000,000+
* **Format:** Apache Parquet (Snappy compression)
* **Encoding:** UTF-8

---

## 🚀 How to Use
```python
from datasets import load_dataset

# Download the sample directly
dataset = load_dataset("Apex-Datasets/German-Wikipedia-Cleaned-Sample")

print(dataset['train'][0])