Ke T5 Small Ko

🚀 ke-t5 base

ke-t5 base是一个在韩语和英语上进行预训练的T5模型。如需了解更多详细信息，请查看Github和论文 韩语论文。

🚀 快速开始

模型信息

模型调用示例

以下是使用该模型的简单示例代码：

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("KETI-AIR/ke-t5-small-ko")
tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-small-ko")

📄 许可证

本项目采用apache-2.0许可证。

📚 详细文档

BibTeX引用

如果您在研究中使用了该模型，请使用以下BibTeX条目进行引用：

@inproceedings{kim-etal-2021-model-cross,
    title = "A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems",
    author = "Kim, San  and
      Jang, Jin Yea  and
      Jung, Minyoung  and
      Shin, Saim",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.33",
    doi = "10.18653/v1/2021.findings-emnlp.33",
    pages = "352--365",
    abstract = "Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems because securing knowledge in the same language with the dialogue system is relatively difficult. Through experiments with a Korean dialogue system, this paper proves that the performance of a non-English dialogue system can be improved by utilizing English knowledge, highlighting the system uses cross-lingual knowledge. For the experiments, we 1) constructed a Korean version of the Wizard of Wikipedia dataset, 2) built Korean-English T5 (KE-T5), a language model pre-trained with Korean and English corpus, and 3) developed a knowledge-grounded Korean dialogue model based on KE-T5. We observed the performance improvement in the open-domain Korean dialogue model even only English knowledge was given. The experimental results showed that the knowledge inherent in cross-lingual language models can be helpful for generating responses in open dialogue systems.",
}