Update README.md

README.md

@@ -19,6 +19,7 @@ You can use this model with Transformers *pipeline* for masked token prediction.
 >>> unmasker = pipeline('fill-mask', model='Davlan/oyo-bert')
 >>> unmasker("Ọjọ kẹsan-an, [MASK] Kẹjọ ni wọn ri oku Baba")
 
+[{'score': 0.9981744289398193, 'token': 3785, 'token_str': 'osu', 'sequence': 'ojo kesan - an, osu kejo ni won ri oku baba'}, {'score': 0.0015279919607564807, 'token': 3355, 'token_str': 'ojo', 'sequence': 'ojo kesan - an, ojo kejo ni won ri oku baba'}, {'score': 0.0001734074903652072, 'token': 11780, 'token_str': 'osun', 'sequence': 'ojo kesan - an, osun kejo ni won ri oku baba'}, {'score': 9.066923666978255e-05, 'token': 21579, 'token_str': 'oṣu', 'sequence': 'ojo kesan - an, oṣu kejo ni won ri oku baba'}, {'score': 1.816015355871059e-05, 'token': 3387, 'token_str': 'odun', 'sequence': 'ojo kesan - an, odun kejo ni won ri oku baba'}]
 ```
 
 ### Acknowledgment