Update README.md

Added example usage

README.md

@@ -15,6 +15,29 @@ This is a finetuning of a MarianMT pretrained on English-Chinese. The target lan
 The first phase of training (mixed) is performed on a dataset containing both English-Chinese and English-Vietnamese sentences.
 The second phase of training (pure) is performed on a dataset containing only English-Vietnamese sentences.
 
+### Example
+```
+%%capture
+!pip install transformers transformers[sentencepiece]
+
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+# Download the pretrained model for English-Vietnamese available on the hub
+model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/en-vi")
+
+tokenizer = AutoTokenizer.from_pretrained("CLAck/en-vi")
+# Download a tokenizer that can tokenize English since the model Tokenizer doesn't know anymore how to do it
+# We used the one coming from the initial model
+# This tokenizer is used to tokenize the input sentence
+tokenizer_en = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-zh')
+# These special tokens are needed to reproduce the original tokenizer
+tokenizer_en.add_tokens(["<2zh>", "<2vi>"], special_tokens=True)
+
+sentence = "The cat is on the table"
+# This token is needed to identify the target language
+input_sentence = "<2vi> " + sentence 
+translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True))
+output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
+```
 
 ### Training results