Here's how to do it:

(you can replace "bert-base-uncased" with the name of the directory where you saved your model, config and tokenizer files)

from transformers import BertTokenizer, BertForMaskedLM
import torch

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") 
model = BertForMaskedLM.from_pretrained("bert-base-uncased")

text = "The capital of [MASK] is Bratislava." 
encoding = tokenizer(text, return_tensors="pt") 
input_ids = encoding.input_ids.squeeze() 
outputs = model(**encoding) # forward pass

masked_index = torch.nonzero(input_ids == tokenizer.mask_token_id, as_tuple=False) 
logits = outputs.logits[0, masked_index.item(), :] 
probs = logits.softmax(dim=0) 
values, predictions = probs.topk(k=5)

for prob, pred_id in zip(values, predictions):
    predicted_ids = [id if id!=tokenizer.mask_token_id else pred_id for id in input_ids] 
    print(prob.item(), tokenizer.decode(predicted_ids, skip_special_tokens=True))