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# Import required libraries
import torch
from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel
# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# Encode a text inputs
text = "What is 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]
# 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_text)
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