 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='distilbert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.")  [{'sequence': "[CLS] hello i'm a role model. [SEP]",   'score': 0.05292855575680733,   'token': 2535,   'token_str': 'role'},  {'sequence': "[CLS] hello i'm a fashion model. [SEP]",   'score': 0.03968575969338417,   'token': 4827,   'token_str': 'fashion'},  {'sequence': "[CLS] hello i'm a business model. [SEP]",   'score': 0.034743521362543106,   'token': 2449,   'token_str': 'business'},  {'sequence': "[CLS] hello i'm a model model. [SEP]",   'score': 0.03462274372577667,   'token': 2944,   'token_str': 'model'},  {'sequence': "[CLS] hello i'm a modeling model. [SEP]",   'score': 0.018145186826586723,   'token': 11643,   'token_str': 'modeling'}] ```  Here is how to use this model to get the features of a given text in PyTorch:  ```python from transformers import DistilBertTokenizer, DistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = DistilBertModel.from_pretrained("distilbert-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 DistilBertTokenizer, TFDistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ```  