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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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# EnViT5-base |
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State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese and English used in [MTet's paper](https://arxiv.org/abs/2210.05610). |
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## How to use |
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For more details, do check out [our Github repo](https://github.com/vietai/mtet). |
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[Finetunning examples 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/envit5-base") |
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model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/envit5-base") |
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model.cuda() |
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``` |
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## Citation |
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``` |
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@misc{mtet, |
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doi = {10.48550/ARXIV.2210.05610}, |
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url = {https://arxiv.org/abs/2210.05610}, |
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author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang}, |
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keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {MTet: Multi-domain Translation for English and Vietnamese}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
