Ke T5 Large Ko

🚀 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。該模型為自然語言處理任務提供了強大的支持，尤其適用於跨語言的相關場景。若想了解更多詳細信息，請查看Github和論文 韓語論文。

🚀 快速開始

模型和分詞器的加載

from transformers import AutoModel, AutoTokenizer

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

📄 許可證

本項目採用Apache - 2.0許可證。

📚 詳細文檔

BibTeX引用和引用信息

@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.",
}

模型相關信息

小工具示例

- 輸入文本: 아버지가 방에 들어가신다.</s>