TatarNLPWorld/tatar-news-analysis-multiclass

Tatar News Multiclass Classification Dataset

A comprehensive multi-class classification dataset for categorizing Tatar language news articles into various topics, curated by TatarNLPWorld as part of the Tat2Vec project.

📖 Overview

This dataset contains 86,963 Tatar language texts classified into 9 distinct categories. Each entry includes the full content, title, category, source, and publication date. It's specifically designed for training and evaluating multi-class text classification models for Tatar language natural language processing tasks.

📊 Dataset Statistics

General Information

Metric	Value
Total Records	86,963
Records with Title	86,963 (100%)
Records with Date	86,963 (100%)
Total Characters	182,133,054
Average Content Length	2,094 characters
Median Content Length	780 characters
Content Length Range	50 - 10,000 characters
Date Range	2008 - 2026
Number of Categories	9

Class Distribution

Category (Tatar)	Category (English)	Count	Percentage
Башка	Other	28,894	33.2%
Җәмгыять	Society	8,882	10.2%
Әдәбият	Literature	7,668	8.8%
Мәгариф	Education	7,511	8.6%
Милләт	Nation	7,378	8.5%
Сәясәт	Politics	7,144	8.2%
Мәдәният	Culture	6,641	7.6%
Шоу-бизнес	Show Business	6,440	7.4%
Дин	Religion	6,405	7.4%
Total		86,963	100%

📁 Data Structure

Each record contains the following fields:

Field	Type	Description
content	string	Full article content in Tatar language
title	string	Article title
category	string	Topic category (9 unique categories)
content_length	integer	Length of content in characters
resource	string	Source URL or identifier
date	string	Publication date

🚀 Usage Example

Loading the Dataset

from datasets import load_dataset
from collections import Counter

# Load the dataset (add your token if the dataset is private)
dataset = load_dataset("TatarNLPWorld/tatar-news-analysis-multiclass")

# Access train and validation splits
train_data = dataset["train"]
validation_data = dataset["validation"]

print(f"Training samples: {len(train_data)}")
print(f"Validation samples: {len(validation_data)}")

# Check class distribution
train_labels = train_data["category"]
distribution = Counter(train_labels)
print(f"Class distribution: {dict(distribution)}")

# Check top categories
print("\nTop 5 categories:")
for category, count in distribution.most_common(5):
    print(f"  {category}: {count} ({count/len(train_data)*100:.1f}%)")

Expected Output:

Training samples: 78266
Validation samples: 8697
Class distribution: {'Әдәбият': 6901, 'Башка': 26004, 'Мәдәният': 5977, 'Сәясәт': 6430, 'Дин': 5764, 'Мәгариф': 6760, 'Җәмгыять': 7994, 'Милләт': 6640, 'Шоу-бизнес': 5796}

Top 5 categories:
  Башка: 26004 (33.2%)
  Җәмгыять: 7994 (10.2%)
  Әдәбият: 6901 (8.8%)
  Мәгариф: 6760 (8.6%)
  Милләт: 6640 (8.5%)

Sample Record

{
    "content": "Татарстан Республикасында мәгариф системасын үстерү буенча яңа проектлар гамәлгә ашырыла. 2025 елда 50 яңа мәктәп төзеләчәк...",
    "title": "Татарстанда мәгариф өлкәсендә зур үзгәрешләр көтелә",
    "category": "Мәгариф",
    "content_length": 1250,
    "resource": "https://tatar-inform.tatar/news/example",
    "date": "2025-09-15"
}

🔬 Applications

This dataset supports multiple NLP tasks:

• Multi-class Text Classification: Topic categorization of Tatar news
• Token Classification: Named entity recognition, part-of-speech tagging
• Feature Extraction: Creating embeddings for Tatar language texts
• Sentence Similarity: Comparing semantic similarity between texts
• Text Generation: Language modeling and text generation tasks
• Summarization: Generating summaries of Tatar news/articles
• Zero-shot Classification: Cross-lingual and zero-shot learning experiments

📊 Dataset Splits

The dataset is automatically stratified split into:

• Training Set: 90% (78,266 samples)
• Validation Set: 10% (8,697 samples)

The splits maintain the original class distribution.

🛠️ Data Collection

This dataset is part of the Tat2Vec project and was curated from various Tatar language news sources and publications spanning from 2008 to 2026, ensuring:

• Authentic Tatar language content
• Diverse topics across 9 categories
• Large-scale coverage with nearly 87,000 samples
• Wide temporal diversity

📜 License

This dataset is released under the MIT License, allowing for:

• Commercial use
• Modification
• Distribution
• Private use

🤝 Citation

If you use this dataset in your research or projects, please cite:

@dataset{tat2vec_multiclass_2026,
    title = {Tatar News Multiclass Classification Dataset},
    author = {TatarNLPWorld},
    year = {2026},
    publisher = {Hugging Face},
    version = {1.0.0},
    note = {Part of the Tat2Vec project},
    url = {https://huggingface.co/datasets/TatarNLPWorld/tatar-news-analysis-multiclass}
}

👥 Team and Maintenance

This dataset is maintained by TatarNLPWorld, a community dedicated to advancing Natural Language Processing for the Tatar language through open-source resources and collaboration. It is part of the larger Tat2Vec project initiative.

Contributors

• TatarNLPWorld Team

📬 Contact and Contributions

We welcome contributions and feedback!

• Issues: Please open an issue on the Hugging Face repository
• Contributions: Submit pull requests for improvements
• Contact: Reach out through the TatarNLPWorld community channels

🌟 Acknowledgments

Special thanks to all data sources and contributors who made this dataset possible, supporting the development of Tatar language NLP resources through the Tat2Vec project.

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