A binary text classification dataset for Tatar news articles, designed to support tasks such as sentiment analysis, topic detection, or category classification (e.g., positive/negative, relevant/irrelevant). The dataset contains short news excerpts in the Tatar language with binary labels, collected from publicly available online sources. It is intended for training and evaluating text classification models for the Tatar language, a low-resource Turkic language.

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 excerpts, article fragments) 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 annotations, metadata, and compilation 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 texts 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-binary
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:

Training binary text classifiers for Tatar
Sentiment analysis (positive/negative) of news content
Topic relevance classification
Benchmarking machine learning models on low-resource Turkic languages
Cross-lingual transfer learning experiments

Out-of-Scope Use

This dataset should not be used for:

Redistribution of verbatim news excerpts without respecting original copyrights
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

Each record contains:

Field	Type	Description
`id`	string	Unique identifier for the news excerpt
`text`	string	The news text (or excerpt) in Tatar
`label`	int64	Binary label (0 or 1)
`source`	string	Original source/website (if available)
`date`	string	Publication date in ISO format (if available)

Data Splits

Split	Description
`train`	Training set (exact size not yet specified)

Dataset Creation

Curation Rationale

Tatar language resources for text classification are scarce. This dataset was created to provide a benchmark for binary classification tasks (e.g., sentiment, topic) on Tatar news texts, enabling researchers to train and evaluate models for this low-resource language and to promote the development of Tatar NLP applications.

Source Data

Data Collection and Processing

The texts were collected from publicly available Tatar news websites and portals, such as Intertat, Tatar-inform, Azatliq, and others. The collection process involved:

Web crawling of news sections
Extraction of article text and metadata
Filtering to short excerpts or headlines (depending on the task design)
Manual or semi-automatic labeling (details below)
Deduplication and cleaning

Who are the source data producers?

The original texts were produced by journalists, editors, and media organizations. The TatarNLPWorld community performed the labeling and structured the dataset.

Annotations

Annotation process

The binary labels were assigned according to specific guidelines (e.g., sentiment polarity, topic relevance). The process included:

Initial automatic labeling using heuristic rules or pre-trained models
Manual verification by Tatar-speaking annotators
Resolution of disagreements by discussion
Final quality control

Who are the annotators?

Annotators were Tatar language speakers with experience in linguistics or NLP, recruited from the TatarNLPWorld community.

Personal and Sensitive Information

The dataset contains publicly available news texts; no personal or sensitive information beyond what appears in the news is intentionally included. Users should still exercise caution when redistributing verbatim texts.

Bias, Risks, and Limitations

Domain bias: The dataset may overrepresent certain news categories or sources.
Label noise: Manual labeling may introduce inconsistencies.
Temporal coverage: The corpus reflects news from a specific time window.
Copyright constraints: The underlying texts are protected; users must respect original rights (see Licensing & Legal Notice).

Recommendations

Be aware of possible label imbalance and apply appropriate evaluation metrics.
Use the dataset for research purposes rather than production systems without further validation.
Cite the original sources when using excerpts.

Citation

BibTeX (dataset):

@dataset{tatar_news_analysis_binary_2026,
    title = {Tatar News Analysis Binary},
    author = {TatarNLPWorld Community},
    year = {2026},
    publisher = {Hugging Face},
    url = {https://huggingface.co/datasets/TatarNLPWorld/tatar-news-analysis-binary}
}

APA (dataset): TatarNLPWorld Community. (2026). Tatar News Analysis Binary [Data set]. Hugging Face. https://huggingface.co/datasets/TatarNLPWorld/tatar-news-analysis-binary

Related Publication

The following paper describes a related text classification approach:

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

Binary classification – a task where each input is assigned one of two possible categories.
Low-resource language – a language with limited digital corpora and NLP tools.

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

Downloads last month: 20

Total file size:

16.2 MB