Create README.md

README.md

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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}
+}
+
+```