CMLI-NLP/MiTC

# MiTC

## Introduction

[MiLMo](https://github.com/CMLI-NLP/MiLMo) constructs a minority multilingual text classification dataset named MiTC which contains five languages, including Mongolian, Tibetan, Uyghur, Kazakh and Korean.

We also use [MiLMo](https://github.com/CMLI-NLP/MiLMo) for the downstream experiment of text classification on MiTC.

## Hugging Face

https://huggingface.co/datasets/CMLI-NLP/MiTC

## Citation

Plain Text:
J. Deng, H. Shi, X. Yu, W. Bao, Y. Sun and X. Zhao, "MiLMo:Minority Multilingual Pre-Trained Language Model," 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Honolulu, Oahu, HI, USA, 2023, pp. 329-334, doi: 10.1109/SMC53992.2023.10393961.

BibTeX:
```
@INPROCEEDINGS{10393961,
author={Deng, Junjie and Shi, Hanru and Yu, Xinhe and Bao, Wugedele and Sun, Yuan and Zhao, Xiaobing},
booktitle={2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
title={MiLMo:Minority Multilingual Pre-Trained Language Model},
year={2023},
volume={},
number={},
pages={329-334},
keywords={Soft sensors;Text categorization;Social sciences;Government;Data acquisition;Morphology;Data models;Multilingual;Pre-trained language model;Datasets;Word2vec},
doi={10.1109/SMC53992.2023.10393961}}
```

## Disclaimer

This dataset/model is for academic research purposes only. Prohibited for any commercial or unethical purposes.

# MiTC

## 引言

[MiLMo](https://github.com/CMLI-NLP/MiLMo)构建了一款名为MiTC的少数民族多语种文本分类数据集，该数据集涵盖蒙古语、藏语、维吾尔语、哈萨克语与朝鲜语共五种语言。

我们在MiTC数据集上开展文本分类下游实验时，同时采用了MiLMo模型。

## Hugging Face 数据集页面

https://huggingface.co/datasets/CMLI-NLP/MiTC

## 引用格式

### 纯文本格式

邓俊杰、施涵如、于鑫赫、鲍吴格德勒、孙源、赵小兵, "MiLMo：少数民族多语种预训练语言模型", 2023年IEEE系统、人与控制论国际会议（SMC）, 美国夏威夷州瓦胡岛火奴鲁鲁, 2023年, 第329-334页, DOI: 10.1109/SMC53992.2023.10393961.

### BibTeX格式

@INPROCEEDINGS{10393961,
author={Deng, Junjie and Shi, Hanru and Yu, Xinhe and Bao, Wugedele and Sun, Yuan and Zhao, Xiaobing},
booktitle={2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
title={MiLMo:Minority Multilingual Pre-Trained Language Model},
year={2023},
volume={},
number={},
pages={329-334},
keywords={软传感器;文本分类;社会科学;政府;数据采集;形态学;数据模型;多语种;预训练语言模型;数据集;Word2vec},
doi={10.1109/SMC53992.2023.10393961}}

## 免责声明

本数据集/模型仅用于学术研究，严禁用于任何商业或不道德用途。