将上述的描述提供实例代码
时间: 2023-03-08 07:07:31 浏览: 49
答:下面是一个使用ChatGPT的基本示例代码:import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('openai-gpt')# Encode context the generation is conditioned on
text = "The meaning of life is"
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('openai-gpt')# 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 (in our case, the word 'meaning')
predicted_index = torch.argmax(predictions[0, -1, :]).item()
predicted_text = tokenizer.decode(indexed_tokens + [predicted_index])# Print the predicted word
print(predicted_text) # The meaning of life is