MrBERT

>>> from transformers import pipeline
>>> from pprint import pprint
>>> unmasker = pipeline('fill-mask', model='BSC-LT/MrBERT')

>>> pprint(unmasker("I love the<mask>of Barcelona.",top_k=3))
[{'score': 0.29333314299583435,
  'sequence': 'I love the city of Barcelona.',
  'token': 31489,
  'token_str': 'city'},
 {'score': 0.06682543456554413,
  'sequence': 'I love the capital of Barcelona.',
  'token': 10859,
  'token_str': 'capital'},
 {'score': 0.05594080686569214,
  'sequence': 'I love the streets of Barcelona.',
  'token': 178738,
  'token_str': 'streets'}]
>>> pprint(unmasker("Me encanta la<mask>de Barcelona.",top_k=3))
[{'score': 0.4422685205936432,
  'sequence': 'Me encanta la ciudad de Barcelona.',
  'token': 19587,
  'token_str': 'ciudad'},
 {'score': 0.059732843190431595,
  'sequence': 'Me encanta la capital de Barcelona.',
  'token': 10859,
  'token_str': 'capital'},
 {'score': 0.03484857454895973,
  'sequence': 'Me encanta la arquitectura de Barcelona.',
  'token': 83374,
  'token_str': 'arquitectura'}]
>>> pprint(unmasker("M'encanta la<mask>de Barcelona.",top_k=3))
[{'score': 0.45476993918418884,
  'sequence': "M'encanta la ciutat de Barcelona.",
  'token': 17128,
  'token_str': 'ciutat'},
 {'score': 0.05597861483693123,
  'sequence': "M'encanta la capital de Barcelona.",
  'token': 10859,
  'token_str': 'capital'},
 {'score': 0.04105329513549805,
  'sequence': "M'encanta la música de Barcelona.",
  'token': 16051,
  'token_str': 'música'}]