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.

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}
}