README.md

metadata

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.

How to use

For more details, do check out our Github repo.

Finetunning examples can be found here.

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}
}