| --- | |
| language: | |
| - vi | |
| tags: | |
| - t5 | |
| - seq2seq | |
| # Machine translation for vietnamese | |
| ## Model Description | |
| T5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architecture. | |
| ## Training data | |
| T5-vi-en-base was trained on 4M sentence pairs (english,vietnamese) | |
| ### How to use | |
| ```py | |
| from transformers import T5ForConditionalGeneration, 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 = T5ForConditionalGeneration.from_pretrained("NlpHUST/t5-vi-en-base") | |
| tokenizer = T5Tokenizer.from_pretrained("NlpHUST/t5-vi-en-base") | |
| model.to(device) | |
| src = "Theo lãnh đạo Sở Y tế, 3 người này không có triệu chứng sốt, ho, khó thở, đã được lấy mẫu xét nghiệm và cách ly tập trung." | |
| tokenized_text = tokenizer.encode(src, return_tensors="pt").to(device) | |
| model.eval() | |
| summary_ids = model.generate( | |
| tokenized_text, | |
| max_length=256, | |
| 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) | |
| According to the head of the Department of Health, the three people had no symptoms of fever, cough, shortness of breath, were taken samples for testing and concentrated quarantine. | |
| ``` |