README.md

T5: Text-To-Text Transfer Transformer for english vietnamese translation#### Example Using```

import torch

from transformers import MT5ForConditionalGeneration, T5Tokenizer import torch if torch.cuda.is_available():
device = torch.device("cuda")

print('There are %d GPU(s) available.' % torch.cuda.device_count())

print('We will use the GPU:', torch.cuda.get_device_name(0))

else: print('No GPU available, using the CPU instead.') device = torch.device("cpu")

model = MT5ForConditionalGeneration.from_pretrained("NlpHUST/t5-en-vi-small") tokenizer = T5Tokenizer.from_pretrained("NlpHUST/t5-en-vi-small") model.to(device)

src = "In school , we spent a lot of time studying the history of Kim Il-Sung , but we never learned much about the outside world , except that America , South Korea , Japan are the enemies ." tokenized_text = tokenizer.encode(src, return_tensors="pt").to(device) model.eval() summary_ids = model.generate( tokenized_text, max_length=128, num_beams=5, repetition_penalty=2.5, length_penalty=1.0, early_stopping=True ) output = tokenizer.decode(summary_ids[0], skip_special_tokens=True) print(output) ``````Ở trường, chúng tôi dành nhiều thời gian để nghiên cứu về lịch sử Kim Il-Sung, nhưng chúng tôi chưa bao giờ học được nhiều về thế giới bên ngoài, ngoại trừ Mỹ, Hàn Quốc, Nhật Bản là kẻ thù.```