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:

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) and Bender et al. (2021)). 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), which was developed and released in the context of the same research paper 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 for further information.

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

Metrics

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Results

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Model Examination

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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

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Compute Infrastructure

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Hardware

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Software

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Citation

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 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.",
}

@article{2020t5,
  author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
  title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {140},
  pages   = {1-67},
  url     = {http://jmlr.org/papers/v21/20-074.html}
}

APA:

- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.

Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Korea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

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How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-base-ko")
 
model = AutoModelForSeq2SeqLM.from_pretrained("KETI-AIR/ke-t5-base-ko")