The largest open corpus for the Tatar language with over 1 million documents collected from news websites, social media, articles, books, and Wikipedia. Designed for various NLP tasks including language modeling, text classification, information extraction, and search.

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 articles, Wikipedia pages, books, social media posts) remain the property of their respective authors and publishers. They are not owned by the TatarNLPWorld team and are not covered by the CC BY‑SA 4.0 license or any other open license applied to the compilation.
The structured compilation, metadata, and any original annotations created by the TatarNLPWorld community 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 documents 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-web-corpus
Paper(s):
- 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
- Burnashev, R. A., Gilmullin, R. A., & Arabov, M. K. (2026). Parameter-efficient fine-tuning of large language models for Tatar language: a comparative study of LoRA and QLoRA. In 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (pp. 1–4). IEEE. https://doi.org/10.1109/USBEREIT70063.2026.11580544
- Arabov, M. K., Gainullin, R. N., & Khusaenov, A. I. (2026). Modelling Subword Embeddings of Low-Resource Languages for the Digitalisation of Industry 4.0 Industrial Systems. In 2026 International Russian Smart Industry Conference (SmartIndustryCon) (pp. 74–79). IEEE. https://doi.org/10.1109/SmartIndustryCon68821.2026.11492814

Uses

Direct Use

The corpus is intended for a wide range of NLP tasks:

Language modeling (training GPT, BERT, etc.)
Text classification (topic, sentiment, category)
Token classification (NER, POS tagging)
Summarization of Tatar texts
Text generation and feature extraction
Zero-shot classification and cross-lingual transfer
Word embeddings (Word2Vec, FastText) using the text field

Out-of-Scope Use

Redistribution of verbatim documents without permission from original copyright holders.
Any use that violates the rights of content creators whose texts are included.
Creation of misleading or harmful content based on the corpus.
Use as a source for factual or real-time information – the corpus is a static snapshot and may contain errors or outdated content.

Dataset Structure

Data Fields

Each record contains:

Field	Type	Description
`title`	string	Document title (98.2% availability)
`content`	string	Main text content
`category`	string	Topic category (97.9% availability)
`source`	string	Source URL or identifier (100% availability)
`text`	string	Combined field: `title + "\n\n" + content` (ready for modeling)

Data Splits

Split	Size	Description
`full`	1,053,698	Complete corpus
`sample`	1,000	Small subset for testing and exploration

Dataset Creation

Curation Rationale

Tatar is a low-resource Turkic language with a scarcity of large-scale digital corpora. This dataset was assembled to provide a comprehensive resource for training and evaluating NLP models, fostering research and applications for the Tatar-speaking community.

Source Data

Data Collection and Processing

The corpus was collected from 21 diverse sources, including:

Wikipedia (tt.wikipedia.org) – 80.97% of documents
News portals (Intertat, Tatar-inform, Shähri Qazan, Vatanym Tatarstan, etc.)
Magazines (Söyembikä, Qazan utları)
Social media (VK, Telegram)
Books (Telegram channels and digital libraries)
Other web resources

Processing steps:

Web crawling of public Tatar-language websites
HTML extraction and cleaning (removal of tags, scripts, ads)
Language filtering (keep only Tatar Cyrillic texts)
Deduplication and normalization
Creation of the text field by combining title and content

Who are the source data producers?

The original texts were created by Wikipedia editors, journalists, authors, bloggers, and social media users. The compilation and structuring were performed by TatarNLPWorld, but the intellectual content remains with the original creators.

Annotations

No manual annotations were added. The category field was derived from the source metadata (e.g., article category, website section).

Personal and Sensitive Information

The dataset contains publicly available texts. No intentional collection of personal or sensitive data was performed. If any personal information appears in the documents, it is solely because it was present in the original public content. No anonymization was applied.

Bias, Risks, and Limitations

Source imbalance: Wikipedia dominates (80.97%), which may bias language models toward encyclopedic style.
Domain bias: Certain news topics or book genres may be overrepresented.
Temporal coverage: The corpus reflects content available up to early 2026; no date metadata is included.
Copyright constraints: Users must respect original copyrights (see Licensing & Legal Notice).
Potential noise: Web data may contain formatting artifacts, spam, or code-switching.

Recommendations

For balanced language modeling, consider sampling from different sources or using weighted loss.
Use the source field to filter or stratify data.
For commercial applications, verify the copyright status of individual documents.
Cite the original sources when using excerpts.

Citation

BibTeX (dataset):

@dataset{tatar_web_corpus_2026,
    title = {Tatar Web Corpus},
    author = {TatarNLPWorld Community},
    year = {2026},
    publisher = {Hugging Face},
    url = {https://huggingface.co/datasets/TatarNLPWorld/tatar-web-corpus}
}

APA (dataset): TatarNLPWorld Community. (2026). Tatar Web Corpus [Data set]. Hugging Face. https://huggingface.co/datasets/TatarNLPWorld/tatar-web-corpus

Related Publications

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}
}

@inproceedings{burnashev2026parameter,
  author = {Burnashev, R. A. and Gilmullin, R. A. and Arabov, M. K.},
  title = {Parameter-efficient fine-tuning of large language models for Tatar language: a comparative study of LoRA and QLoRA},
  booktitle = {2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)},
  year = {2026},
  pages = {1--4},
  doi = {10.1109/USBEREIT70063.2026.11580544}
}

@inproceedings{arabov2026subword,
  author = {Arabov, M. K. and Gainullin, R. N. and Khusaenov, A. I.},
  title = {Modelling Subword Embeddings of Low-Resource Languages for the Digitalisation of Industry 4.0 Industrial Systems},
  booktitle = {2026 International Russian Smart Industry Conference (SmartIndustryCon)},
  year = {2026},
  pages = {74--79},
  doi = {10.1109/SmartIndustryCon68821.2026.11492814}
}

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
Burnashev, R. A., Gilmullin, R. A., & Arabov, M. K. (2026). Parameter-efficient fine-tuning of large language models for Tatar language: a comparative study of LoRA and QLoRA. In 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) (pp. 1–4). IEEE. https://doi.org/10.1109/USBEREIT70063.2026.11580544
Arabov, M. K., Gainullin, R. N., & Khusaenov, A. I. (2026). Modelling Subword Embeddings of Low-Resource Languages for the Digitalisation of Industry 4.0 Industrial Systems. In 2026 International Russian Smart Industry Conference (SmartIndustryCon) (pp. 74–79). IEEE. https://doi.org/10.1109/SmartIndustryCon68821.2026.11492814

Glossary

Token – a unit of text (word or subword) used in NLP models.
Low-resource language – a language with limited digital corpora and tools.
Corpus – a large collection of texts used for linguistic analysis.

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

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