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--- |
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language: vi |
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datasets: |
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- cc100 |
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tags: |
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- summarization |
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- translation |
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- question-answering |
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license: mit |
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--- |
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# ViT5-base |
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State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese. |
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## How to use |
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For more details, do check out [our Github repo](https://github.com/vietai/ViT5). |
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[Finetunning Example can be found here](https://github.com/vietai/ViT5/tree/main/finetunning_huggingface). |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base") |
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model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base") |
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model.cuda() |
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``` |
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## Citation |
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``` |
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@inproceedings{phan-etal-2022-vit5, |
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title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation", |
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author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.", |
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop", |
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year = "2022", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.naacl-srw.18", |
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pages = "136--142", |
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} |
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``` |
