| --- |
| language: ba |
| license: mit |
| tags: |
| - bashkir |
| - low-resource-language |
| - continual-training |
| - news |
| - llm-adaptation |
| pretty_name: Bashqort Raw Corpus |
| size_categories: |
| - 10M<n<100M tokens |
| --- |
| |
| # 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](https://bashgazet.ru/articles) | daily socio-political newspaper | ~1,400 articles | |
| | [neftcity.ru](https://neftcity.ru) | regional news | ~548 articles | |
| | [bash.news](https://bash.news) | news & analytics | ~68,708 articles | |
| | [bashkir-corpus](https://github.com/nevmenandr/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: |
|
|
| ```json |
| { |
| "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: |
| ```bibtex |
| |
| @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. |
| |
