 How to use  You can use this model directly with a pipeline for masked language modeling:  ```python >>> 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'}] ```  Here is how to use this model to get the features of a given text in PyTorch:  ```python 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) ```  and in TensorFlow:  ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = TFBertModel.from_pretrained("bert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ```  