license: mit
task_categories:
- text-classification
- token-classification
- feature-extraction
- sentence-similarity
- text-generation
- summarization
- zero-shot-classification
language:
- tt
- tr
tags:
- tatar-language
- news-classification
- binary-classification
- tatar-nlp
pretty_name: Tatar News vs Analytics Binary Classification
Tatar News vs Analytics Binary Classification Dataset
A comprehensive binary classification dataset for distinguishing between news articles and analytical content in the Tatar language, curated by TatarNLPWorld as part of the Tat2Vec project.
📖 Overview
This dataset contains 10,490 Tatar language texts labeled for binary classification between news articles and analytical content. Each entry includes the full content, title, category, source, and publication date. It's specifically designed for training and evaluating text classification models for Tatar language natural language processing tasks.
📊 Dataset Statistics
General Information
| Metric | Value |
|---|---|
| Total Records | 10,490 |
| Records with Title | 10,490 (100%) |
| Records with Date | 10,490 (100%) |
| Total Characters | 24,154,756 |
| Average Content Length | 2,303 characters |
| Median Content Length | 1,559 characters |
| Content Length Range | 50 - 9,997 characters |
| Date Range | 2009 - 2026 |
Class Distribution
| Class | Count | Percentage |
|---|---|---|
| News | 5,245 | 50.0% |
| Analytics | 5,245 | 50.0% |
| Total | 10,490 | 100% |
Note: The dataset is perfectly balanced with exactly 50% news and 50% analytics content.
Category Distribution
| Category (Tatar) | Category (English) | Count | Percentage |
|---|---|---|---|
| Яңалыклар | News | 4,854 | 46.3% |
| Җәмгыять | Society | 2,082 | 19.8% |
| Мәдәният | Culture | 1,562 | 14.9% |
| Дин | Religion | 495 | 4.7% |
| Центральные новости | Central News | 357 | 3.4% |
| Мәгариф | Education | 252 | 2.4% |
| Милләт | Nation | 228 | 2.2% |
| Сәясәт | Politics | 207 | 2.0% |
| Әдәбият | Literature | 191 | 1.8% |
| Икътисад | Economics | 109 | 1.0% |
| Other categories | 153 | 1.5% |
Total Categories: 13 unique categories
📁 Data Structure
Each record contains the following fields:
| Field | Type | Description |
|---|---|---|
content |
string | Full article content in Tatar language |
title |
string | Article title |
label |
integer | Binary label (0: news, 1: analytics) |
label_text |
string | Text representation ("news" or "analytics") |
category |
string | Original category from source (13 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-binary")
# 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["label_text"]
distribution = Counter(train_labels)
print(f"Class distribution: {dict(distribution)}")
# Check category distribution
categories = train_data["category"]
category_dist = Counter(categories)
print("\nTop 5 categories:")
for category, count in category_dist.most_common(5):
print(f" {category}: {count} ({count/len(train_data)*100:.1f}%)")
Expected Output:
Training samples: 9441
Validation samples: 1049
Class distribution: {'news': 4721, 'analytics': 4720}
Top 5 categories:
Яңалыклар: 4367 (46.3%)
Җәмгыять: 1875 (19.9%)
Мәдәният: 1403 (14.9%)
Дин: 445 (4.7%)
Центральные новости: 321 (3.4%)
Sample Record
{
"content": "Татарстан Республикасында илкүләм проектларны актив рәвештә гамәлгә ашыру дәвам итә. 2025 елның тугыз аенда 11 илкүләм проект буенча үтәлеш 29,3 миллиард сум тәшкил иткән...",
"title": "Татарстанда илкүләм проектлар буенча еллык планның өчтән ике өлеше үтәлде",
"label": 1,
"label_text": "analytics",
"category": "Икътисад",
"content_length": 1058,
"resource": "https://tatar-inform.tatar/news/example",
"date": "2025-10-16"
}
🔬 Applications
This dataset supports multiple NLP tasks:
- Text Classification: News vs analytics binary classification
- 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% (9,441 samples)
- Validation Set: ~10% (1,049 samples)
The splits maintain the original balanced class distribution (approximately 50-50).
🛠️ Data Collection
This dataset is part of the Tat2Vec project and was curated from various Tatar language news sources and analytical publications spanning from 2009 to 2026, ensuring:
- Authentic Tatar language content
- Diverse topics across 13 categories
- Perfectly balanced class representation
- 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_binary_2026,
title = {Tatar News vs Analytics Binary 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-binary}
}
👥 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.
Part of the Tat2Vec project - Advancing Tatar Language Processing