Update README.md

Added example usage

README.md

@@ -13,6 +13,30 @@ metrics:
 
 This model is pretrained on Chinese and Indonesian languages, and fine-tuned on Indonesian language.
 
+### 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/indo-mixed")
+
+tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-mixed")
+# 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>", "<2indo>"], special_tokens=True)
+
+sentence = "The cat is on the table"
+# This token is needed to identify the target language
+input_sentence = "<2indo> " + 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
 MIXED