import random import logging logging.basicConfig(level=logging.INFO) import torch from pytorch_transformers import GPT2Tokenizer from pytorch_transformers import GPT2LMHeadModel # 选择 top-k 的函数的实现, def select_top_k(predictions, k=10): predicted_index = random.choice( predictions[0, -1, :].sort(descending=True)[1][:10]).item() return predicted_index # 载入预训练模型的分词器 tokenizer = GPT2Tokenizer.from_pretrained('gpt2') # 使用 GPT2Tokenizer 对输入进行编码 text = "Yesterday, a man named Jack said he saw an alien," indexed_tokens = tokenizer.encode(text) tokens_tensor = torch.tensor([indexed_tokens]) # 读取 GPT-2 预训练模型 model = GPT2LMHeadModel.from_pretrained("gpt2") model.eval() total_predicted_text = text n = 100 # 预测过程的循环次数 for _ in range(n): with torch.no_grad(): outputs = model(tokens_tensor) predictions = outputs[0] predicted_index = select_top_k(predictions, k=10) predicted_text = tokenizer.decode(indexed_tokens + [predicted_index]) total_predicted_text += tokenizer.decode(predicted_index) if '<|endoftext|>' in total_predicted_text: # 如果出现文本结束标志,就结束文本生成 break indexed_tokens += [predicted_index] tokens_tensor = torch.tensor([indexed_tokens]) print(total_predicted_text)注释代码,并说明实现的功能
时间: 2024-03-02 17:48:59 浏览: 29
以下是对代码的注释和功能说明:
```python
import random
import logging
# 设置日志级别为 INFO
logging.basicConfig(level=logging.INFO)
import torch
from pytorch_transformers import GPT2Tokenizer
from pytorch_transformers import GPT2LMHeadModel
# 定义选择 top-k 的函数,返回的是预测的 token 索引
def select_top_k(predictions, k=10):
predicted_index = random.choice(predictions[0, -1, :].sort(descending=True)[1][:10]).item()
return predicted_index
# 载入预训练模型的分词器
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# 使用 GPT2Tokenizer 对输入进行编码
text = "Yesterday, a man named Jack said he saw an alien,"
indexed_tokens = tokenizer.encode(text)
tokens_tensor = torch.tensor([indexed_tokens])
# 读取 GPT-2 预训练模型
model = GPT2LMHeadModel.from_pretrained("gpt2")
model.eval()
total_predicted_text = text
n = 100 # 预测过程的循环次数
# 进行文本生成的循环
for _ in range(n):
with torch.no_grad():
outputs = model(tokens_tensor)
predictions = outputs[0]
# 选择 top-k 的 token 索引
predicted_index = select_top_k(predictions, k=10)
predicted_text = tokenizer.decode(indexed_tokens + [predicted_index])
total_predicted_text += tokenizer.decode(predicted_index)
# 如果出现文本结束标志,就结束文本生成
if '