Add usage example

README.md

@@ -1,5 +1,28 @@
 ## Overview
-This model is a finetuned version of [mt5-small](https://huggingface.co/google/mt5-small) for question paraphrasing task in Turkish. As a generator model, its capabiities are currently investigated and there is an ongoing effort to further improve it.
+This model is a finetuned version of [mt5-small](https://huggingface.co/google/mt5-small) for question paraphrasing task in Turkish. As a generator model, its capabilities are currently investigated and there is an ongoing effort to further improve it. 
+
+## Usage
+You can generate 5 paraphrases for the input question with The simple code below.
+
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+model_name = "mys/mt5-small-turkish-question-paraphrasing"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+tokens = tokenizer.encode_plus("Yarın toplantı kaçta başlıyor?", return_tensors='pt')
+paraphrases = model.generate(tokens['input_ids'], max_length=128, num_return_sequences=5, num_beams=5)
+tokenizer.batch_decode(paraphrases, skip_special_tokens=True)
+```
+
+And the output will be something like:
+```shell
+['Yarın toplantı ne zaman başlıyor?',
+ 'Yarın toplantı saat kaçta başlıyor?',
+ 'Yarın toplantı saat kaçta başlar?',
+ 'Yarın toplantı ne zaman başlayacak?',
+ 'Yarın toplantı ne zaman başlar?']
+```
 
 ## Dataset
 I used [TQP dataset V0.1](https://github.com/monatis/tqp) that I've published just recently. This model should be taken as as a baseline model for TQP dataset. A cleaning and further improvements in the dataset and an elaborate hyperparameter tuning may boost the performance.