The Tatar News Multiclass Classification Dataset contains 86,963 Tatar language news articles classified into 9 distinct topic categories. Each entry includes the full article content, title, category (numeric label and text label), source URL, publication date, and content length. The dataset is specifically designed for training and evaluating multi-class text classification models for Tatar, a low-resource Turkic language. It is part of the Tat2Vec project by TatarNLPWorld.

Curated by: TatarNLPWorld Community
Language(s) (NLP): Tatar (tt)
License: other – see Licensing & Legal Notice below.

Licensing & Legal Notice

This dataset follows the practice established by large web-crawled corpora such as HPLT and OSCAR:

Original source texts (news articles, titles, etc.) remain the property of their respective authors and publishers. They are not owned by the TatarNLPWorld team and are not covered by the MIT license or any other open license applied to the annotations.
The structured compilation, metadata, and any original annotations created by the TatarNLPWorld community are released under the MIT License.
Users are solely responsible for ensuring their use of the underlying texts complies with applicable copyright law. For commercial use of verbatim excerpts, permission from the original copyright holders may be required.
A notice-and-takedown procedure is in place: rights holders can request removal of specific articles by contacting the dataset maintainers (see Dataset Card Contact). We commit to responding within 14 business days and removing disputed content in the next release.

Dataset Sources

Repository: https://huggingface.co/datasets/TatarNLPWorld/tatar-news-analysis-multiclass
Paper: Arabov, M. K., Gilmullin, R. A., & Burnashev, R. A. (2026). Combining Classical and Transformer-based Approaches for Text Classification and Topic Modeling of the Tatar Language. In 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (pp. 1–4). IEEE. https://doi.org/10.1109/USBEREIT70063.2026.11580632
Demo: [Coming Soon]

Uses

Direct Use

This dataset is intended for:

Multi-class text classification (9 categories)
Topic modeling and clustering
Feature extraction for embedding Tatar texts
Sentence similarity and semantic search
Text generation and language modeling
Summarization of Tatar news articles
Zero-shot classification and cross-lingual experiments

Out-of-Scope Use

This dataset should not be used for:

Redistribution of verbatim news articles without permission from original copyright holders
Any use that could misrepresent the original authors' intent (e.g., altering labels without validation)
Applications requiring real-time or up-to-date news content – the dataset is a static snapshot
Automated decision-making in sensitive domains without human oversight

Dataset Structure

Data Fields

Field	Type	Description
`content`	string	Full article content in Tatar language
`title`	string	Article title
`label`	int64	Numeric class label (0–8)
`label_text`	string	Text class label (Tatar category name)
`category`	string	Original category string from source (may differ from `label_text`)
`content_length`	int32	Length of content in characters
`resource`	string	Source URL or identifier
`date`	string	Publication date (ISO format)

Data Splits

The dataset is split using stratified sampling to preserve class distribution:

Split	Size	Percentage
`train`	78,266	90%
`validation`	8,697	10%

Dataset Creation

Curation Rationale

Tatar is a low-resource language with limited labeled datasets for topic classification. This dataset was created to provide a high-quality, large-scale benchmark for multi-class classification of Tatar news articles, enabling research and development of NLP tools for the language.

Source Data

Data Collection and Processing

The texts were collected from various Tatar language news sources and publications from 2008 to 2026. The pipeline included:

Web crawling of publicly available Tatar news websites
Extraction of article content, titles, URLs, and dates
Assignment of categories from source metadata (RSS feeds, site structure)
Cleaning (removal of HTML tags, extra whitespace)
Calculation of content_length and deduplication

Who are the source data producers?

The original texts were created by journalists, editors, and media organizations from Tatar‑language outlets. The TatarNLPWorld community performed the collection, structuring, and categorization, but the underlying content remains the property of the original authors.

Annotations

The category labels were derived automatically from the source websites' metadata (e.g., RSS categories, section names). The numeric label and label_text were assigned by mapping categories to a fixed set of 9 classes. No manual annotation was performed.

Personal and Sensitive Information

The dataset consists solely of publicly available news articles and does not intentionally include personal or sensitive information. No anonymization was performed because the data is already public.

Bias, Risks, and Limitations

Domain bias: The dataset may overrepresent certain sources or time periods.
Label noise: Categories extracted from metadata may contain occasional misclassifications.
Temporal coverage: Articles span 2008–2026, but distribution across years may be uneven.
Copyright constraints: The underlying texts are protected; users must respect original rights (see Licensing & Legal Notice).

Recommendations

Use stratified metrics to account for class imbalance.
Validate performance on your specific target domain.
For commercial applications, verify the copyright status of articles before reuse.

Citation

BibTeX (dataset):

@dataset{tatar_news_multiclass_2026,
    title = {Tatar News Multiclass Classification Dataset},
    author = {TatarNLPWorld Community},
    year = {2026},
    publisher = {Hugging Face},
    url = {https://huggingface.co/datasets/TatarNLPWorld/tatar-news-analysis-multiclass}
}

APA (dataset): TatarNLPWorld Community. (2026). Tatar News Multiclass Classification Dataset [Data set]. Hugging Face. https://huggingface.co/datasets/TatarNLPWorld/tatar-news-analysis-multiclass

Related Publication

The following paper used this dataset or a similar version:

BibTeX:

@inproceedings{arabov2026combining,
  author = {Arabov, M. K. and Gilmullin, R. A. and Burnashev, R. A.},
  title = {Combining Classical and Transformer-based Approaches for Text Classification and Topic Modeling of the Tatar Language},
  booktitle = {2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)},
  year = {2026},
  pages = {1--4},
  doi = {10.1109/USBEREIT70063.2026.11580632}
}

APA: Arabov, M. K., Gilmullin, R. A., & Burnashev, R. A. (2026). Combining Classical and Transformer-based Approaches for Text Classification and Topic Modeling of the Tatar Language. In 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (pp. 1–4). IEEE. https://doi.org/10.1109/USBEREIT70063.2026.11580632

Glossary

Multi-class classification – a task where each input is assigned exactly one of more than two categories.
Stratified split – splitting method that preserves the class distribution in each subset.

More Information

For questions, contributions, or feedback, please open an issue on the Hugging Face repository or contact the TatarNLPWorld community.

Dataset Card Authors

TatarNLPWorld Community

Dataset Card Contact

Hugging Face: https://huggingface.co/TatarNLPWorld
GitHub: https://github.com/TatarNLPWorld
Email: marabov@kpfu.ru

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