Hugging Face's logo

Datasets:
yeshpanovrustem
/
kazparov

Tasks:
Translation
Modalities:
Text
Formats:
parquet
Languages:
Kazakh
Russian
Size:
10K - 100K
Libraries:
Datasets
pandas
Dataset card Data Studio Files
xet
Community
1

You need to agree to share your contact information to access this dataset

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Use of this dataset for commercial purposes is strictly prohibited.
Please provide your real details when requesting access.
The owner of the dataset reserves the right to reject any request without explanation.

Log in or Sign Up to review the conditions and access this dataset content.

🔄 Updates

  • 2025-12-04: Added a new set of Kazakh–English sentence pairs.

KazParov

KazParov—short for Kazakh parallel corpus for various languages—is a Kazakh-centred parallel corpus aligned with multiple languages. The name is pronounced like the surname of Garry Kasparov, the legendary chess grandmaster.

At the current stage, the corpus consists predominantly of Kazakh–Russian sentence pairs. This is due to the relative accessibility of high-quality Kazakh–Russian parallel data compared to, for example, Kazakh–English material. However, since Russian is a high-resource language comparable to English, it may also serve as a useful pivot language in future machine translation and cross-lingual NLP research. Importantly, KazParov is not a synthetic corpus—all sentence pairs are sourced from real texts rather than machine-generated data.

The corpus is released strictly for research and educational purposes only. Any form of commercial use, redistribution for profit, or integration into proprietary systems is not permitted.

Initiated independently during a period of unemployment, KazParov represents one of several personal efforts to enrich Kazakh NLP resources through perseverance and self-driven research. Please excuse the gradual pace of its expansion and updates, as the project is currently being developed single-handedly.

Please also note that the corpus is not entirely error-free, although considerable effort has been made to review, correct, and refine sentence pairs. The primary focus during data collection has been to preserve semantic equivalence rather than strict grammatical or orthographic accuracy. Should you notice any errors or inconsistencies, your feedback and suggestions will be sincerely appreciated.

Downloads last month
20