Create README.md

README.md

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+# 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.```