This dataset contains 17,897 Bashkir-language news and analytical articles annotated with 19 thematic categories for multiclass text classification tasks. Each article belongs to exactly one category. The categories range from news and society to culture, education, and sports. The dataset was created to support NLP research and application development for the Bashkir language, a low-resource Turkic language.

Curated by: Arabov Mullosharaf Kurbonovich, Khaybullina Svetlana Sergeevna (BashkirNLPWorld)
Language(s) (NLP): Bashkir (ba), Cyrillic script
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, analytical pieces) remain the property of their respective authors and publishers. They are not owned by the BashkirNLPWorld team and are not covered by the CC BY‑SA 4.0 license.
The structured compilation, metadata, and any original annotations created during dataset preparation are released under the CC BY‑SA 4.0 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 content 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/BashkirNLPWorld/bashkir-news-multiclass
Related datasets:

Uses

Direct Use

This dataset is suitable for:

Multiclass text classification (19 classes)
Training classifiers for Bashkir topic categorization
Fine-tuning multilingual models for Bashkir text classification
Baseline evaluation for NLP tasks in Bashkir

Out-of-Scope Use

The dataset should not be used for multi‑label classification (use the multilabel version instead).
It is not intended for binary news vs analytics tasks (use the binary version).
Not suitable for tasks requiring fine-grained genre distinctions beyond the defined categories.

Dataset Structure

Data Fields

Field	Type	Description
`content`	string	Full article text
`title`	string	Article title
`category`	string	Thematic category (one of 19 classes)
`content_length`	int64	Length of the text in characters
`resource`	string	Original URL or resource identifier (if available)
`date`	string	Publication date (when available)

Data Splits

The dataset contains a single split (train) with all 17,897 examples. Users are encouraged to create their own train/validation/test splits.

Categories and Distribution

Category (Bashkir)	Category (English)	Count	Percentage
Яңылыҡтар	News	8,497	47.5%
Йәмғиәт	Society	2,827	15.8%
Мәғариф	Education	747	4.2%
Аңлатма	Explanation	708	4.0%
Татарстан	Tatarstan	697	3.9%
Мәҙәниәт	Culture	536	3.0%
Юмор	Humor	479	2.7%
Махсус хәрби операция	Special Military Operation	396	2.2%
Дин	Religion	378	2.1%
Еңеү	Victory	370	2.1%
Әҙәбиәт	Literature	359	2.0%
Хикәйә	Fiction	352	2.0%
Донъя	World	324	1.8%
Конкурстар	Competitions	312	1.7%
Сәләмәтлек	Health	251	1.4%
Иҡтисад	Economy	177	1.0%
Спорт	Sports	172	1.0%
Тарих	History	159	0.9%
Башҡортостан	Bashkortostan	156	0.9%

Dataset Creation

Curation Rationale

The goal was to create a comprehensive multiclass classification dataset for Bashkir covering major thematic categories found in news and analytical content. Unlike the binary version or the multilabel version, this dataset assigns each article to exactly one primary category, making it suitable for standard multiclass classification tasks.

Source Data

Data Collection and Processing

Articles were collected from 14 Bashkir online sources. Processing steps included:

Extracting JSONL files from raw HTML.
Removing texts shorter than 50 or longer than 10,000 characters.
Removing exact duplicates.
Normalizing category names (e.g., яңалыклар → Яңылыҡтар).
Selecting categories with ≥150 examples.
Filtering articles belonging to the selected categories.

Who are the source data producers?

The articles were originally written by journalists, authors, and contributors of the respective online publications. The BashkirNLPWorld team does not claim ownership of the content. The original texts remain the property of their authors and publishers.

Annotations

No manual annotations were added. Categories were derived automatically from normalized category labels.

Personal and Sensitive Information

The texts are public news articles and do not intentionally contain personally identifiable information beyond what is already published.

Bias, Risks, and Limitations

Class imbalance: News articles dominate (47.5%), while some categories have very few examples (e.g., Bashkortostan – 156).
Source bias: The dataset is dominated by certain sources (e.g., azatliqorg accounts for 28% of data).
Geographic bias: Separate categories for Tatarstan and Bashkortostan may reflect regional focus.
Genre bias: All texts are from news sources; may not represent other domains.
Date incompleteness: Many articles lack publication dates.

Recommendations

For class imbalance, use weighted loss functions or oversampling.
Be aware of source distribution when applying to new sources.
For categories with limited examples, consider grouping or few‑shot learning.

Citation

BibTeX (dataset):

@dataset{arabov2026bashkirmulticlass,
  author       = {Arabov, Mullosharaf Kurbonovich and Khaybullina, Svetlana Sergeevna},
  title        = {Bashkir News Multiclass Classification Dataset},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-multiclass}
}

APA (dataset): Arabov, M. K., & Khaybullina, S. S. (2026). Bashkir News Multiclass Classification Dataset [Data set]. Hugging Face. https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-multiclass

Glossary

Multiclass classification – a task where each input is assigned exactly one of more than two categories.
Low-resource language – a language with limited digital resources and tools.

More Information

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

Dataset Card Authors

Arabov Mullosharaf Kurbonovich
Khaybullina Svetlana Sergeevna
BashkirNLPWorld

Dataset Card Contact

For questions, takedown requests, or collaboration, please open an issue on the Hugging Face repository:
https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-multiclass/discussions
📧 Direct email: cool.araby@gmail.com

Downloads last month: 13

Total file size:

32.7 MB