This dataset contains 16,994 Bashkir-language news and analytical articles labeled for binary classification: news (label=1) vs analytics (label=0). The dataset is perfectly balanced with 8,497 examples in each class. It was created to support NLP research and applications 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-binary
Related datasets:

Uses

Direct Use

This dataset is suitable for:

Binary text classification (news vs analytics)
Training binary classifiers (logistic regression, SVM, transformers)
Baseline evaluation for text classification tasks in Bashkir
Fine-tuning multilingual models for news detection

Out-of-Scope Use

The dataset should not be used for multi-class classification tasks (use the multiclass version instead).
It is not intended for multi-label classification (use the multilabel version).
Not suitable for tasks requiring genre-specific analytics (e.g., opinion detection, satire identification).
Redistribution of verbatim texts without permission from original copyright holders is prohibited.

Dataset Structure

Data Fields

Field	Type	Description
`content`	string	Full article text
`title`	string	Article title
`label`	int64	Binary label (1 = news, 0 = analytics)
`label_text`	string	Human-readable label ("news" or "analytics")
`category`	string	Original normalized category (e.g., "Яңылыҡтар", "Йәмғиәт")
`content_length`	int64	Length of the text in characters
`resource`	string	Original URL or resource identifier (if available)
`date`	string	Publication date (when available)

Label Definitions

Label	Value	Description
News	1	Articles reporting current events, news updates, and immediate information
Analytics	0	Investigative pieces, opinion articles, analyses, and feature stories

Data Splits

The dataset contains a single split (train) with all 16,994 examples. The split is perfectly balanced:

News articles (label=1): 8,497 (50%)
Analytics articles (label=0): 8,497 (50%)

Users are encouraged to create their own train/validation/test splits.

Dataset Creation

Curation Rationale

The goal was to create a clean, balanced dataset for binary classification that distinguishes between time‑sensitive news reporting and more analytical/investigative content. This distinction is fundamental in many NLP applications, such as summarization, information retrieval, and content filtering.

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).
Creating binary labels based on normalized categories.
Balancing classes using undersampling (removing 7,434 analytics articles to match the news class size).

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. Labels were derived automatically from normalized categories:

All news variants → label=1
All other categories → label=0

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

Undersampling: Removing 7,434 analytics articles may lead to loss of diversity in the analytics class.
Definition of analytics: The binary split is based on category labels, which may not perfectly align with the intuitive distinction between news and analysis.
Source bias: The dataset is dominated by certain sources (e.g., azatliqorg accounts for 28% of data).
Genre bias: All texts are from news sources; may not represent other domains.
Date incompleteness: Many articles lack publication dates.
Copyright constraints: The underlying texts are protected; users must respect original rights (see Licensing & Legal Notice).

Recommendations

Consider alternative balancing methods (e.g., weighted loss, oversampling) to mitigate undersampling effects.
For domain-specific applications, filter articles by source or category.
For tasks requiring genre diversity, supplement with additional sources.

Citation

BibTeX (dataset):

@dataset{arabov2026bashkirbinary,
  author       = {Arabov, Mullosharaf Kurbonovich and Khaybullina, Svetlana Sergeevna},
  title        = {Bashkir News Binary Classification Dataset},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-binary}
}

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

Glossary

Binary classification – a task where each input is assigned one of two possible 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-binary/discussions
📧 Direct email: cool.araby@gmail.com

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