---
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 used in [MTet's paper](https://arxiv.org/abs/2210.05610).

## How to use
For more details, do check out [our Github repo](https://github.com/vietai/mtet). 

[Finetunning examples 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}
}

```