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winogrande-mk / README.md
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---
language:
- mk
---
# WinoGrande MK version
This dataset is a Macedonian adaptation of the [WinoGrande dataset](https://huggingface.co/datasets/gordicaleksa/serbian-llm-eval-v1), originally curated (English -> Serbian) by Aleksa Gordić. It was translated from Serbian to Macedonian using the [Google Translate API](https://cloud.google.com/translate).
You can find this dataset as part of the macedonian-llm-eval [GitHub](https://github.com/LVSTCK/macedonian-llm-eval) and [HuggingFace](https://huggingface.co/datasets/LVSTCK/macedonian-llm-eval).
## Why Translate from Serbian?
The Serbian dataset was selected as the source instead of English because Serbian and Macedonian are closer from a linguistic standpoint, making Serbian a better starting point for translation. Additionally, the Serbian dataset was refined using GPT-4, which, according to the original report, significantly improved the quality of the translation. Note that this is an assumption that needs further validation (quantitative).. a small quality check was conducted on the translated Macedonian dataset, and the results were deemed to be of good quality.
## Overview
- **Language**: Macedonian
- **Source**: Serbian LLM Evaluation Dataset v1
- **Translation**: Serbian -> Macedonian (via Google Translate API)
- **Split**: Only the test split is available.
- **Purpose**: Benchmark and evaluate large language models in Macedonian.
## Credits
- Serbian version of the dataset: [Aleksa Gordić](https://huggingface.co/datasets/gordicaleksa/serbian-llm-eval-v1)
- Translation: [Google Translate API](https://cloud.google.com/translate)
- Original dataset - WinoGrande Paper: [WinoGrande: An Adversarial Winograd Schema Challenge at Scale](https://arxiv.org/abs/1907.10641)
## Limitations
- Translation may contain minor inaccuracies.
- Only the test split is provided.
## Citation
If you use this dataset, please cite the original WinoGrande dataset:
```
@InProceedings{ai2:winogrande,
title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
},
year={2019}
}
```