Create README.md
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README.md
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---
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license: cc-by-sa-4.0
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tags:
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- wikipedia
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- tigre
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- tig
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- corpus
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- machine learning
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- low-resource
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language:
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- tig
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---
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# 🇪🇷 Tigre Wikipedia (tigwiki) Corpus
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The **Tigre Wikipedia Corpus** is a foundational resource containing all non-template articles from **tig.wikipedia.org**. This dataset is essential for developing Natural Language Processing (NLP) models, such as language models, machine translation systems, and text generation tools for the **Tigre language (ISO 639-3: tig)**.
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This project represents the dedicated effort of the Tigre diaspora community to build a comprehensive digital knowledge base.
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## 🌟 Background and Scope
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The Tigre language Wikipedia was **approved and went live in December 2024**, marking a significant milestone for the digital presence of the language. This initial corpus is the result of a collaboration of more than **twenty diaspora volunteers** who continue to actively contribute content.
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### Primary Sections Covered
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The portal currently organizes its content into the following key sections:
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* Let's explore our country
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* Tourism
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* Art and Culture
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* Politics
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* Books
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* Science and Technology
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* History
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* Health
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* Biography
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* Miscellaneous
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* Sport
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* Fun
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* Reference
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* Tigre Language
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* Food and Drinks
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* Pictures
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## 📖 Dataset Structure
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The corpus is provided as a single, compressed JSON Lines file (`tigre_wikipedia.jsonl.gz`). Each line represents one cleaned Wikipedia article.
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### Data Fields
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| Feature | Type | Description |
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| :--- | :--- | :--- |
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| **id** | `string` | The unique ID of the Wikipedia article. |
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| **title** | `string` | The title of the article (e.g., "History of Eritrea"). |
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| **text** | `string` | The **cleaned, plain text content** of the article (Wikitext and metadata removed). |
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## 🚀 Usage
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You can load this dataset directly using the Hugging Face `datasets` library.
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```python
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from datasets import load_dataset
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# Replace 'your_username' with your Hugging Face username
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dataset = load_dataset("your_username/tigre_wikipedia")
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# Example: Accessing the first article
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print(dataset['train'][0]['title'])
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print(dataset['train'][0]['text'][:500] + "...")
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