Vit5 Base

🚀 ViT5-base

State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.

🚀 Quick Start

This is a state-of-the-art pre-trained encoder-decoder model based on the Transformer architecture, specifically designed for the Vietnamese language.

✨ Features

• Datasets: Utilizes the cc100 dataset.
• Tags: Applicable for summarization, translation, and question-answering tasks.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base")  
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base")
model.cuda()

For more details, do check out our Github repo.

Finetunning Example can be found here.

📚 Documentation

No detailed documentation content is provided in the original document.

🔧 Technical Details

No technical details are provided in the original document.

📄 License

This project is licensed under the MIT license.

📚 Citation

@inproceedings{phan-etal-2022-vit5,
    title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation",
    author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
    year = "2022",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-srw.18",
    pages = "136--142",
}