readme: 
  ---
  language: ko
  license: apache-2.0
  tags:
  - t5
  eos_token: </s>
  widget:
  - text: 아버지가 방에 들어가신다.</s>
  ---
  
  # Model Card for ke-t5-base-ko 
   
   
  # Model Details
   
  ## Model Description
   
   
  - **Developed by:** Korea Electronics Technology Institute Artificial Intelligence Research Center
  - **Shared by [Optional]:** More information needed
  - **Model type:** Text2Text Generation
  - **Language(s) (NLP):** More information needed
  - **License:** More information needed
  - **Related Models:**
    - **Parent Model:** T5
  - **Resources for more information:** 
       - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints)
       - [KE-T5 Github Repo](https://github.com/AIRC-KETI/ke-t5)
       - [Paper](https://aclanthology.org/2021.findings-emnlp.33/)
       - [Associated Paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf)
       - [Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
   
   
  # Uses
   
   
  ## Direct Use
   
  This model can be used for the task of Text2Text Generation
   
  ## Downstream Use [Optional]
   
  More information needed
   
  ## Out-of-Scope Use
   
  The model should not be used to intentionally create hostile or alienating environments for people.
   
  # Bias, Risks, and Limitations
   
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
   
   
  ## Recommendations
   
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
   
   
  # Training Details
   
  ## Training Data
   
  The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5.
   
  The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**.
   
   See the [t5-base model card](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) for further information.
   
  ## Training Procedure
   
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed
   
  ### Factors
   
   
  ### Metrics
   
  More information needed
  ## Results 
   
  More information needed
   
  # Model Examination
   
  More information needed
   
  # Environmental Impact
   
   
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **Hardware Type:** More information needed
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
   
  # Technical Specifications [optional]
   
  ## Model Architecture and Objective
   
  More information needed
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
  More information needed
   
  # Citation
   
   **BibTeX:**
   
   ```bibtex
  @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