metuKKhud/bashqort-raw

Bashqort Raw Corpus

Description

This dataset contains raw Bashkir text collected for continual training of large language models (LLMs). It is part of the project "Adapting Open-Source LLMs for the Bashkir Language", which aims to evaluate adaptation methods proposed by LlamaTurk (Toraman, 2024) and Persian adaptation (Mahdizadeh Sani et al., 2024).

The corpus is assembled from multiple sources to provide a diverse linguistic foundation for language modeling.

Sources

Source	Type	# articles / tokens
bashgazet.ru	daily socio-political newspaper	~1,400 articles
neftcity.ru	regional news	~548 articles
bash.news	news & analytics	~68,708 articles
bashkir-corpus	mixed (public domain + shuffled)	~20.9M tokens

Total tokens: see dataset statistics on Hugging Face.

Preprocessing

• Deduplication (document and sentence level)
• Removal of sentences with <5 words
• Removal of HTML artifacts, ads, and metadata
• No train/val split (the whole corpus is for self-supervised learning)

Format

Each example is a JSON object with the following fields:

{
  "text": "Bashkir sentence or paragraph",
  "source": "bashgazet.ru | neftcity.ru | bash.news | bash-corpus | etc",
  "is_shuffled": true/false
}

For plain text mode, the dataset also provides a text column with raw sentences. Intended Use

Continual pre‑training / further pretraining of LLMs (e.g., Llama‑2, Llama‑3)

Next‑token prediction (causal language modeling)

Any research aiming to improve Bashkir language representation in NLP

Licensing

This dataset is released under the MIT License. Citation

If you use this dataset, please cite the project repository:

@misc{khudiakova2025bashqortraw,
  author       = {Khudiakova, Kseniia},
  title        = {Bashqort Raw Corpus: Bashkir Text Collection for Continual Training},
  year         = {2025},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/metuKKhud/bashqort-raw}
}

Additionally, consider citing the original data sources and the adaptation framework:

Toraman, C. (2024). LlamaTurk: Adapting open-source generative large language models for low-resource language. arXiv:2405.07745.

Mahdizadeh Sani, S., et al. (2024). Extending LLMs to new languages: A case study of Llama and Persian adaptation. arXiv:2412.13375.