This dataset contains 97,531 Tajik news articles labeled with one of 12 topic categories, designed for multiclass text classification tasks. The categories are well-balanced and cover major news topics such as Ҷамъият (Society), Иқтисод (Economy), Сиёсат (Politics), Ҳодиса (Incidents), and others. The data have been cleaned, deduplicated, and filtered to ensure consistent quality.

Curated by: Arabov Mullosharaf Kurbonovich
Language(s) (NLP): Tajik (tg)
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 dataset curator 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/TajikNLPWorld/tajik-news-multiclass

Uses

Direct Use

The dataset can be used for:

Supervised multiclass classification of Tajik news texts
Benchmarking Tajik language models
Training classifiers for news topic detection
Educational purposes in NLP for low-resource languages

Out-of-Scope Use

The dataset is not intended for clustering or unsupervised learning (a separate dataset is available for that: TajikNLPWorld/tajik-news-cluster). It also does not contain multiple labels per document.

Dataset Structure

Data Fields

Field	Type	Description
`content`	string	Full text of the article (title + body, concatenated with `\n\n`)
`title`	string	Article headline
`category`	string	One of the 12 topic categories
`content_length`	int64	Length of `content` in characters
`resource`	string	URL of the article (if available)
`date`	string	Publication date (if available; otherwise empty)

Data Splits

The dataset contains a single split (train) with all 97,531 records. For evaluation, users should create their own train/validation/test splits, e.g., 80/10/10.

Categories (12 classes)

Category	Count	Percentage
Ҷамъият	25,448	26.1%
Иқтисод	13,833	14.2%
Сиёсат	12,467	12.8%
Ҳодиса	9,597	9.8%
Амният	8,599	8.8%
Хабарҳо	8,406	8.6%
Фарҳанг	5,424	5.6%
Варзиш	4,240	4.3%
Ҳуқуқ	2,755	2.8%
Ҳукумат	2,653	2.7%
Ҷаҳон	2,489	2.6%
Маориф	1,620	1.7%

Dataset Creation

Curation Rationale

The dataset was created to provide a standardized benchmark for multiclass classification in Tajik, a low-resource language. It aggregates articles from multiple sources and applies a consistent category normalization to reduce noise.

Source Data

Data Collection and Processing

Collection: Articles were collected from eight Tajik news portals between 2015 and 2025.
Cleaning: Removed HTML, extra whitespace, deduplicated, filtered by length (50–10,000 characters).
Category Normalization: Original categories were mapped to a unified set of 12 classes using a rule‑based dictionary and keyword matching; articles with empty or garbage categories were discarded.
Final Format: Each article is stored as a JSON object with the fields above.

Who are the source data producers?

The original content was produced by the respective news portals listed in the cluster dataset card. The dataset is based on publicly available news articles.

Annotations

Categories were derived from the original category field provided by the sources, supplemented with keyword‑based rules. No manual annotation was performed, but the normalization rules were carefully designed to map similar concepts to the same class.

Personal and Sensitive Information

The dataset contains news articles that may mention public figures, but no attempt has been made to remove personal information. All data are already publicly available.

Bias, Risks, and Limitations

Class imbalance: The smallest class (Маориф) has only 1.6% of the data.
Source imbalance: The dataset is dominated by asiaplus.tj (62% of records), which may affect generalization.
Date coverage: Not all articles have a reliable date field.
Language: Tajik (Cyrillic) only.

Recommendations

Use stratified splits to preserve class distribution.
Consider class weights or resampling when training.
Evaluate on a held‑out set from multiple sources.

Citation

BibTeX (dataset):

@misc{arabov2026tajikmulticlass,
  author = {Arabov, Mullosharaf Kurbonovich},
  title = {Tajik News Multiclass Classification Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/TajikNLPWorld/tajik-news-multiclass}
}

APA (dataset): Arabov, M. K. (2026). Tajik News Multiclass Classification Dataset [Data set]. Hugging Face. https://huggingface.co/datasets/TajikNLPWorld/tajik-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 (TajikNLPWorld)

Dataset Card Contact

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

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