Model Card for KEByT5-small (330M #params)

<!-- Provide a quick summary of what the model is/does. --> KEByT5: Korean-Enhanced/Enriched Byte-level Text-to-Text Transfer Transformer(T5)

Cross-modal, Multilingual Friendly Token-free Pretrained Language Model

Acknowledgements

Model Details

본 사전학습 언어모델은 다음과 같은 규모를 가집니다:

특히, small 및 base 모델은 google/byt5-small, google/byt5-base 모델과 동일한 신경망 구조와 크기를 가지며, 토크나이저(ByT5Tokenizer)와 구현 상 두 모델은 별도의 수정없이 바로 교환하여 사용할 수 있습니다. huggingface transformers에서의 사용법 역시, T5ForConditionalGeneration을 동일하게 사용할 수 있습니다.

Model Description

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Model Sources [optional]

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Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> 해당 사전학습 언어모델은 연구 및 교육 목적의 활용으로 그 사용 목적이 제한됩니다.

Direct Use

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현재 공개되는 모델은 T5 모델 학습에 사용된 Corrupted span denoising 만으로 학습되어 있어, 실제 응용 태스크에 적용하기 위해서는 fine-tuning 과정이 필요합니다.

Downstream Use [optional]

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Token-free 모델의 특성 상, 복잡하거나 Noisy한 입력에 강건하며, 짧은 시퀀스 길이의 생성에 적합합니다. (예: 언어 이해, 대화 응답 생성) 사전학습은 1024 bytes 길이의 데이터를 학습했기 때문에, 이를 초과하는 긴 시퀀스를 다루는 문제에 적합하지 않습니다.

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure [optional]

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Preprocessing

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Speeds, Sizes, Times

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

Hardware

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Software

Citation [optional]

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Glossary [optional]

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