README.md

# ViT5-base

State-of-the-art pre-trained Transformer-based encoder-decoder model for Vietnamese.

## How to use
For more details, do check out [our Github repo](https://github.com/justinphan3110/ViT5). 
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
​
tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base")  
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base")
​
sentence = "Xin chào"
text =  "summarize: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
    input_ids=input_ids, attention_mask=attention_masks,
    max_length=256,
    early_stopping=True
)
for output in outputs:
    line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    print(line)
```

## Citation
```
Coming Soon...
```