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---
language: vi
datasets:
- cc100
tags:
- summarization
- translation
- question-answering
license: mit
---
# EnViT5-base
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese and English.
## How to use
For more details, do check out [our Github repo](https://github.com/vietai/mtet).
[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/envit5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/envit5-base")
model.cuda()
```
## Citation
```
@misc{mtet,
doi = {10.48550/ARXIV.2210.05610},
url = {https://arxiv.org/abs/2210.05610},
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},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {MTet: Multi-domain Translation for English and Vietnamese},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |