Create README.md

README.md

@@ -0,0 +1,70 @@
+# 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/).
+
+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`
+
+
+## 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)  | **32.12** (cased) / **32.92** (uncased)
+
+#### Example Using
+
+``` bash
+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)
+```
+#### 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.
+
+```