Datasets:

farabi-lab
/

Code_switching

language:
  - kk
  - ru
license: cc-by-nc-4.0
task_categories:
  - text-generation
tags:
  - kazakh
  - russian
  - code-switching
  - language-normalization
  - kazakh-language
  - informal-communication
  - text-rewriting
  - instruction-following
  - low-resource-language
pretty_name: Kazakh-Russian Code-Switching Normalization Dataset
size_categories:
  - n<1K

🇰🇿 Kazakh-Russian Code-Switching Normalization Dataset

Dataset Summary

Kazakh-Russian Code-Switching Normalization Dataset is a bilingual instruction-following dataset designed for identifying and rewriting Kazakh-Russian mixed-language text into clean Kazakh.

The dataset focuses on informal communication, where Kazakh speakers may naturally mix Russian and Kazakh in one message. Each sample contains a prompt with code-switching, a response that identifies the Russian words or phrases, and a corrected Kazakh version of the original message.

📊 Dataset Statistics

General Metrics

Metric	Count
Total Samples	300
Total Words (approx.)	34,317
Avg. Words per Sample	114

Word Count Distribution Per Field

The following table details the distribution of word counts across different fields in the dataset.

Field	Mean	Median	Min	Max	Total Words
domain	2.0	2.0	2	2	600
prompt	31.1	30.0	20	50	9,323
response	80.3	78.0	49	138	24,094
sample_id	1.0	1.0	1	1	300

Dataset Structure

Each dataset instance contains an informal mixed-language prompt, a detailed response, a domain label, and a sample identifier.

Data Fields

sample_id: A unique identifier for each sample.
prompt: The original informal user text. The prompt usually contains Kazakh-Russian code-switching, where Russian words, phrases, or full clauses appear inside a Kazakh-language message.
response: The expected answer. This field identifies the Russian-language parts of the prompt and provides a corrected version written fully in Kazakh.
domain: The communication domain of the sample. In this dataset, the domain is Informal Communication.

Data Instance

Below is one representative example from the dataset.

  {
    "sample_id": "1",
    "prompt": "Ребят, посоветуйте хороший онлайн-курс по IT. Менің ойымша, қазіргі уақытта бұл өте қажетті мамандық. Хочу освоить что-то новое, развиваться в этом направлении. Қайсысы тиімдірек, ақшаға тұра ма?",
    "response": "Бұл сөйлемде орыс тіліндегі 'ребят', 'посоветуйте хороший онлайн-курс по IT', 'хочу освоить что-то новое, развиваться в этом направлении' сияқты сөздер мен тіркестер қолданылған. Сөйлемнің мәтінін толық қазақшаға аудару арқылы тілдің тазалығын қамтамасыз етуге болады. Дұрыс нұсқа: 'Достар, IT бойынша жақсы онлайн-курс кеңес беріңіздерші. Менің ойымша, қазіргі уақытта бұл өте қажетті мамандық. Жаңа нәрсені үйреніп, осы бағытта дамығым келеді. Қайсысы тиімдірек, ақшасына тұра ма?'",
    "domain": "Informal Communication"
  }

Funding

This dataset was developed as part of the project funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan under Grant No. BR24993001, “Creation of a Large Language Model (LLM) to Support the Kazakh Language and Advance Technological Development.”

Citation

If you use this dataset in your research, please cite the following article:

APA

Kadyrbek, N., Tuimebayev, Z., Mansurova, M., & Viegas, V. (2025). The development of small-scale language models for low-resource languages, with a focus on Kazakh and direct preference optimization. Big Data and Cognitive Computing, 9(5), 137. https://doi.org/10.3390/bdcc9050137

BibTeX

@article{kadyrbek2025development,
  title     = {The Development of Small-Scale Language Models for Low-Resource Languages, with a Focus on Kazakh and Direct Preference Optimization},
  author    = {Kadyrbek, Nurgali and Tuimebayev, Zhanseit and Mansurova, Madina and Viegas, Vitor},
  journal   = {Big Data and Cognitive Computing},
  volume    = {9},
  number    = {5},
  pages     = {137},
  year      = {2025},
  publisher = {MDPI},
  doi       = {10.3390/bdcc9050137},
  url       = {https://www.mdpi.com/2504-2289/9/5/137}
}