How can I show multiple predictions of the next word in a sentence?
I am using the GPT-2 pre trained model. the code I am working on will get a sentence and generate the next word for that sentence. I want to print multiple predictions, like the three first predictions with best probabilities! 
for example if I put in the sentence "I's an interesting ...." 
predictions:  "Books"    "story"    "news"

is there a way I can modify this code to show these predictions instead of one?! 

also there are two parts in the code, I do not understand, what is the meaning of the numbers in  (predictions[0, -1, :])?  and why do we put [0] after predictions = output[0]? 


import torch
from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Encode a text inputs
text = "The fastest car in the  "
indexed_tokens = tokenizer.encode(text)


# Convert indexed tokens in a PyTorch tensor
tokens_tensor = torch.tensor([indexed_tokens])


# Load pre-trained model (weights)
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Set the model in evaluation mode to deactivate the DropOut modules
model.eval()

# If you have a GPU, put everything on cuda
#tokens_tensor = tokens_tensor.to('cuda')
#model.to('cuda')

# Predict all tokens
with torch.no_grad():
    outputs = model(tokens_tensor)
    predictions = outputs[0]
    #print(predictions)


# Get the predicted next sub-word
predicted_index = torch.argmax(predictions[0, -1, :]).item()
predicted_text = tokenizer.decode(indexed_tokens + [predicted_index])

# Print the predicted word
#print(predicted_index)
print(predicted_text)


The result for the above code will be :

The fastest car in the world.


You can use torch.topk as follows:

predicted_indices = [x.item() for x in torch.topk(predictions[0, -1, :],k=3)]
