The Ossetian News Multilabel dataset contains approximately 13,036 manually curated records in the Ossetian language, designed for multi-label classification of news articles. Each record can belong to multiple thematic categories simultaneously (up to 3 labels). The dataset is intended to support NLP research for this low-resource language.

Curated by: Arabov Mullosharaf Kurbonovich (OssetianNLPWorld)
Language(s) (NLP): Ossetian (os)
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) remain the property of their respective authors and publishers. They are not owned by the OssetianNLPWorld team and are not covered by the CC BY 4.0 license.
The structured compilation, metadata, and any original annotations created during dataset preparation are released under the CC BY 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/OssetianNLPWorld/ossetian-news-multilabel

Uses

Direct Use

This dataset is suitable for:

Training multi-label text classification models for Ossetian news
Evaluating tag prediction systems
Studying label co-occurrence patterns
Fine-tuning multilingual models for low-resource language classification tasks

Out-of-Scope Use

The dataset should not be used for single-label classification (use the multiclass version instead).
It is not intended for generating offensive content or any unethical applications.
Redistribution of verbatim texts without permission from original copyright holders is prohibited.

Dataset Structure

Data Fields

Field	Type	Description
`text`	string	Full news text (headline + body) in Ossetian
`labels`	list[string]	List of thematic categories (1 or more per record)
`url`	string	Original source URL of the news article

Label List

The dataset uses 12 labels (in Russian for compatibility):

Культура и Искусство
Общество и Социальная сфера
Политика и Государство
Спорт
Происшествия и Правопорядок
Медицина и ЗОЖ
Образование и Наука
Сельское хозяйство и Экология
Экономика и ЖКХ
История и Память
Туризм и География
Религия и Традиции

Data Splits

The dataset contains a single split (train) with all 13,036 records.

Dataset Creation

Curation Rationale

Ossetian is a low‑resource language, and multi-label classification resources are scarce. This dataset was created to enable research on assigning multiple thematic categories to news articles in Ossetian, reflecting the realistic complexity of news content.

Source Data

Data Collection and Processing

Articles were collected from six Ossetian-language news portals:

alaniatv.ru
iryston.tv
mahdug.ru
rastdzinad.ru
sputnik-ossetia.com
xurzarin.ru

Processing steps included:

Collection of raw news articles.
Manual or semi-automatic labeling with multiple thematic categories.
Cleaning of text and metadata.
Balancing and filtering to the final set of 13,036 records.

Who are the source data producers?

The original texts were produced by journalists and editors of the respective Ossetian media outlets. The labels were curated by the OssetianNLPWorld team.

Annotations

Annotation process

Labels were assigned according to a predefined list of 12 categories. Each article could receive one or more labels. The annotation was performed manually by annotators familiar with Ossetian news content.

Who are the annotators?

Annotators were recruited from the OssetianNLPWorld community or were language specialists involved in the project.

Personal and Sensitive Information

The dataset consists of publicly available news articles. No personal data beyond public mentions of individuals is included. No anonymization was performed.

Bias, Risks, and Limitations

Label sparsity: Most records have only one label (97%); true multi-label examples are rare, which may limit the ability to train models for predicting multiple labels.
Class imbalance: Some labels (e.g., "Культура") are much more frequent than others (e.g., "Религия").
Language mixing: Some articles may contain mixed-language content (Ossetian + Russian).
Copyright constraints: The underlying texts are protected; see Licensing & Legal Notice.

Recommendations

Use techniques for handling label imbalance (e.g., weighted loss functions).
For better multi-label capability, consider oversampling rare multi-label examples.
Respect original copyrights when redistributing excerpts.

Citation

BibTeX (dataset):

@dataset{arabov2026ossetiannewsmultilabel,
  author = {Arabov, Mullosharaf Kurbonovich},
  title = {{Ossetian-News-Multilabel}: A Dataset for Multi-Label Classification of Ossetian News},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/OssetianNLPWorld/ossetian-news-multilabel}
}

APA (dataset): Arabov, M. K. (2026). Ossetian-News-Multilabel: A Dataset for Multi-Label Classification of Ossetian News [Data set]. Hugging Face. https://huggingface.co/datasets/OssetianNLPWorld/ossetian-news-multilabel

Glossary

Multi-label classification – a task where each input can be assigned multiple labels simultaneously.
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 (OssetianNLPWorld)

Dataset Card Contact

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

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