---
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 |


![image](https://cdn-uploads.huggingface.co/production/uploads/64f75f7bd04a890f5347d436/YfeFMgEeKzKBjOFSEp4jB.png)

---

## 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.

```json
  {
    "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](https://doi.org/10.3390/bdcc9050137)

### BibTeX

```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}
}
```