| # T5-EN-VI-BASE:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation |
|
|
| # Dataset |
|
|
| The *IWSLT'15 English-Vietnamese* data is used from [Stanford NLP group](https://nlp.stanford.edu/projects/nmt/). |
|
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| For all experiments the corpus was split into training, development and test set: |
|
|
| | Data set | Sentences | Download |
| | ----------- | --------- | --------------------------------------------------------------------------------------------------------------------------------- |
| | Training | 133,317 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/train-en-vi.tgz) or located in `data/train-en-vi.tgz` |
| | Development | 1,553 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/dev-2012-en-vi.tgz) or located in `data/dev-2012-en-vi.tgz` |
| | Test | 1,268 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/test-2013-en-vi.tgz) or located in `data/test-2013-en-vi.tgz` |
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| ## Results |
|
|
| The results on test set. |
|
|
| | Model | BLEU (Beam Search) |
| | ----------------------------------------------------------------------------------------------------- | ------------------ |
| | [Luong & Manning (2015)](https://nlp.stanford.edu/pubs/luong-manning-iwslt15.pdf) | 23.30 |
| | Sequence-to-sequence model with attention | 26.10 |
| | Neural Phrase-based Machine Translation [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 27.69 |
| | Neural Phrase-based Machine Translation + LM [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 28.07 |
| | t5-en-vi-small (pretraining, without training data) | **28.46** (cased) / **29.23** (uncased) |
| |t5-en-vi-small (fineturning with training data) | **32.38** (cased) / **33.19** (uncased) |
| | t5-en-vi-base (pretraining, without training data) | **29.66** (cased) / **30.37** (uncased) |
| #### Example Using |
|
|
| ``` bash |
| 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 |
|
|
| ``` bash |
| |
| 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. |
| |
| ``` |
| ### Contact information |
| For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com). |
