saillab/alpaca_zulu_taco

---
language:
- zu
pretty_name: Zulu alpaca-52k
size_categories:
- 100K<n<1M
---
This repository contains the dataset used for the TaCo paper.

The dataset follows the style outlined in the TaCo paper, as follows:

```
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
```

Please refer to the paper for more details: [OpenReview](https://openreview.net/forum?id=02MLWBj8HP)

If you have used our dataset, please cite it as follows:

**Citation**
```
@inproceedings{upadhayay2024taco,
title={TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in {LLM}s through Translation-Assisted Chain-of-Thought Processes},
author={Bibek Upadhayay and Vahid Behzadan},
booktitle={5th Workshop on practical ML for limited/low resource settings, ICLR},
year={2024},
url={https://openreview.net/forum?id=02MLWBj8HP}
}
```

The original dataset [(Alpaca-52K)](https://github.com/tatsu-lab/stanford_alpaca?tab=readme-ov-file#data-release) was translated using Google Translate.

**Copyright and Intended Use**
This dataset has been released under CC BY-NC, intended for academic and research purposes only. Please review the licenses and terms and conditions of Alpaca-52K, Dolly-15K, and Google Cloud Translation before using this dataset for any purpose other than research.

---
language:
- zu
pretty_name: 祖鲁语alpaca-52k
size_categories:
- 10万<n<100万
---
本仓库包含用于TaCo论文的数据集。

该数据集遵循TaCo论文中概述的格式，具体如下：

{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}

更多详情请参考该论文：[OpenReview](https://openreview.net/forum?id=02MLWBj8HP)

如果您使用了本数据集，请按以下格式引用：

**引用格式**

@inproceedings{upadhayay2024taco,
title={TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in {LLM}s through Translation-Assisted Chain-of-Thought Processes},
author={Bibek Upadhayay and Vahid Behzadan},
booktitle={5th Workshop on practical ML for limited/low resource settings, ICLR},
year={2024},
url={https://openreview.net/forum?id=02MLWBj8HP}
}

原始数据集[(Alpaca-52K)](https://github.com/tatsu-lab/stanford_alpaca?tab=readme-ov-file#data-release)使用谷歌翻译（Google Translate）进行翻译。

**版权与预期用途**
本数据集已根据CC BY-NC协议发布，仅用于学术和研究目的。在将本数据集用于研究以外的任何目的前，请查阅Alpaca-52K、Dolly-15K和谷歌云翻译（Google Cloud Translation）的许可协议及条款。

原始信息汇总

数据集概述

数据集特征

• instruction: 数据类型为字符串
• input: 数据类型为字符串
• output: 数据类型为字符串
• id: 数据类型为字符串
• text: 数据类型为字符串

数据集分割

• 训练集 (train):
  • 示例数量: 49601
  • 数据大小: 182759939.63885036字节
• 测试集 (test):
  • 示例数量: 12401
  • 数据大小: 45692748.36114964字节

数据集大小

• 下载大小: 116421729字节
• 数据集总大小: 228452688.0字节

数据文件配置

• 默认配置 (default):
  • 训练集路径: data/train-*
  • 测试集路径: data/test-*