T5 En Vi Small

🚀 T5-EN-VI-SMALL: Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation

This project focuses on pretraining a Text-To-Text Transfer Transformer for English - Vietnamese translation, aiming to achieve high - quality translation results.

🚀 Quick Start

If you want to use the t5 - en - vi - small model for English - Vietnamese translation, you can refer to the following example:

import torch

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-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)

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.

✨ Features

• Utilize the IWSLT'15 English - Vietnamese dataset for training and testing.
• Achieve high BLEU scores in English - Vietnamese translation tasks.

📦 Dataset

The IWSLT'15 English - Vietnamese data is used from Stanford NLP group. For all experiments the corpus was split into training, development and test set:

📚 Results

The results on test set.

📄 Contact information

For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).