| | --- |
| | language: vi |
| | datasets: |
| | - cc100 |
| | tags: |
| | - summarization |
| | - translation |
| | - question-answering |
| |
|
| | license: mit |
| | --- |
| | |
| | # ViT5-large |
| |
|
| | 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-large") |
| | model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large") |
| | 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", |
| | } |
| | ``` |
