readme: >>> from transformers import pipeline
  >>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
  >>> unmasker("Hello I'm a [MASK] model.")
  
  [{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
    'score': 0.1073106899857521,
    'token': 4827,
    'token_str': 'fashion'},
   {'sequence': "[CLS] hello i'm a role model. [SEP]",
    'score': 0.08774490654468536,
    'token': 2535,
    'token_str': 'role'},
   {'sequence': "[CLS] hello i'm a new model. [SEP]",
    'score': 0.05338378623127937,
    'token': 2047,
    'token_str': 'new'},
   {'sequence': "[CLS] hello i'm a super model. [SEP]",
    'score': 0.04667217284440994,
    'token': 3565,
    'token_str': 'super'},
   {'sequence': "[CLS] hello i'm a fine model. [SEP]",
    'score': 0.027095865458250046,
    'token': 2986,
    'token_str': 'fine'}]
    
    from transformers import BertTokenizer, BertModel
  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
  model = BertModel.from_pretrained("bert-base-uncased")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)