Ke T5 Large

🚀 ke - t5 base

ke - t5 base是一個在韓語和英語上進行預訓練的T5模型。該模型可用於處理涉及韓語和英語的自然語言處理任務，為跨語言的文本處理提供了強大的支持。如需瞭解更多詳細信息，請查看 Github 和 論文 韓語論文。

🚀 快速開始

模型使用示例

以下是使用該模型的示例代碼：

from transformers import AutoModel, AutoTokenizer

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

📚 詳細文檔

BibTeX引用和引用信息

如果您在研究中使用了該模型，可以使用以下BibTeX條目進行引用：

@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.",
}

📄 許可證

本項目採用Apache - 2.0許可證。