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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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