readme: ---
  language: vi
  datasets:
  - cc100
  tags:
  - summarization
  - translation
  - question-answering
  
  license: mit
  ---
  
  # ViT5-base
  
  State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
  
  ## How to use
  For more details, do check out [our Github repo](https://github.com/vietai/ViT5). 
  
  [Finetunning Example can be found here](https://github.com/vietai/ViT5/tree/main/finetunning_huggingface).
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  ​
  tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base")  
  model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base")
  model.cuda()
  ```
  
  ## 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",
  }
  ```