[自然语言处理|NLP] 文本生成中的应用：从原理到实践

# 使用Transformers库和PyTorch的GPT模型进行文本生成
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# 加载GPT-2模型和分词器
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# 输入文本
input_text = "自然语言处理是"

# 分词和编码
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# 使用GPT-2生成文本
output_ids = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)

# 解码生成的文本
generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

# 输出生成的文本
print(generated_text)

import torch
import torch.nn as nn
import torch.optim as optim

# 定义生成器和鉴别器
class Generator(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(Generator, self).__init__()
        # 定义模型结构

    def forward(self, noise):
        # 模型前向传播
        return generated_text

class Discriminator(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(Discriminator, self).__init__()
        # 定义模型结构

    def forward(self, text):
        # 模型前向传播
        return real_or_fake_prob

# 定义模型参数
vocab_size = 10000
embedding_dim = 256
hidden_dim = 512

# 初始化生成器和鉴别器
generator = Generator(vocab_size, embedding_dim, hidden_dim)
discriminator = Discriminator(vocab_size, embedding_dim, hidden_dim)

# 定义优化器和损失函数
optimizer_G = optim.Adam(generator.parameters(), lr=0.001)
optimizer_D = optim.Adam(discriminator.parameters(), lr=0.001)
criterion = nn.BCELoss()

# 训练模型
for epoch in range(num_epochs):
    for real_text in real_text_data_loader:
        # 训练鉴别器
        # ...

        # 训练生成器
        noise = torch.randn(batch_size, noise_dim)
        generated_text = generator(noise)
        # ...

# 数据预处理示例
def preprocess_text(text):
    tokens = tokenizer.tokenize(text)
    input_ids = tokenizer.convert_tokens_to_ids(tokens)
    return input_ids

# 选择模型示例
model = GPT2LMHeadModel.from_pretrained("gpt2")

# 假设我们有一个文本文件，每行是一段文本
with open("corpus.txt", "r", encoding="utf-8") as file:
    corpus = file.readlines()

from transformers import GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# 分词和编码
encoded_corpus = [tokenizer.encode(text, max_length=512, truncation=True) for text in corpus]

from transformers import GPT2LMHeadModel, GPT2Config

config = GPT2Config.from_pretrained("gpt2")
model = GPT2LMHeadModel(config)

import torch
from torch.utils.data import DataLoader, TensorDataset

# 将数据转换为PyTorch张量
input_ids = torch.tensor(encoded_corpus, dtype=torch.long)

# 构建数据加载器
dataset = TensorDataset(input_ids)
data_loader = DataLoader(dataset, batch_size=4, shuffle=True)

# 定义损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)

# 训练模型
num_epochs = 3
for epoch in range(num_epochs):
    for batch in data_loader:
        inputs = batch[0].to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
        outputs = model(inputs, labels=inputs)
        loss = outputs.loss
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

def generate_text(prompt, max_length=100, temperature=0.7):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    input_ids = input_ids.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
    
    # 生成文本
    output_ids = model.generate(input_ids, max_length=max_length, temperature=temperature)
    
    # 解码生成的文本
    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return generated_text

# 使用生成器生成文本
generated_text = generate_text("人工智能的未来是")
print(generated_text)