自然语言文本分类模型代码

以下是一个基于PyTorch的文本分类模型的示例代码，用于将给定的文本分为多个预定义类别：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TextClassifier(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, num_layers, bidirectional, dropout):
        super().__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, bidirectional=bidirectional, dropout=dropout)
        self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, text, text_lengths):
        embedded = self.dropout(self.embedding(text))
        packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths.to('cpu'), enforce_sorted=False)
        packed_output, (hidden, cell) = self.rnn(packed_embedded)
        output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)
        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1) if self.rnn.bidirectional else hidden[-1,:,:])
        return self.fc(hidden.squeeze(0))

该模型将输入的文本作为整数序列传递给嵌入层，然后通过多层LSTM层进行处理，最终输出每个类别的预测概率。

在训练模型之前，需要将文本序列转换为整数标记，通常使用分词器/标记器完成此任务。另外还需要定义优化器和损失函数来训练模型。

以下是一个完整的训练脚本的示例：

import torch.optim as optim
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torch.utils.data import DataLoader
from torchtext.data.utils import ngrams_iterator
from torchtext.data.utils import get_tokenizer
from torch.utils.data.dataset import random_split
from collections import Counter

# 获取数据集和分词器
train_iter = AG_NEWS(split='train')
tokenizer = get_tokenizer('basic_english')

# 构建词汇表
counter = Counter()
for (label, line) in train_iter:
    counter.update(tokenizer(line))
vocab = build_vocab_from_iterator([counter])
vocab.set_default_index(vocab['<unk>'])

# 定义标记化函数和文本处理函数
def yield_tokens(data_iter):
    for _, text in data_iter:
        yield tokenizer(text)
        
def text_transform(tokenizer, vocab, data):
    """将文本数据转换为张量数据"""
    data = [vocab[token] for token in tokenizer(data)]
    return torch.tensor(data)
    
# 定义批次生成器
def collate_batch(batch):
    label_list, text_list, offsets = [], [], [0]
    for (_label, _text) in batch:
        label_list.append(_label-1)
        processed_text = torch.cat([text_transform(tokenizer, vocab, _text), torch.tensor([vocab['<eos>']])])
        text_list.append(processed_text)
        offsets.append(processed_text.size(0))
    label_list = torch.tensor(label_list)
    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
    text_list = torch.cat(text_list)
    return label_list, text_list, offsets

# 构建数据集和数据加载器
train_iter, test_iter = AG_NEWS()
train_iter = list(train_iter)
test_iter = list(test_iter)
train_dataset = list(map(lambda x: (x[0], x[1]), train_iter))
test_dataset = list(map(lambda x: (x[0], x[1]), test_iter))
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, collate_fn=collate_batch)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True, collate_fn=collate_batch)

# 创建模型和优化器
model = TextClassifier(len(vocab), 64, 128, 4, 2, True, 0.5)
optimizer = optim.Adam(model.parameters())

# 定义损失函数和训练函数
criterion = nn.CrossEntropyLoss()

def train(model, iterator, optimizer, criterion):
    epoch_loss = 0
    model.train()
    for (label, text, offsets) in iterator:
        optimizer.zero_grad()
        predictions = model(text, offsets)
        loss = criterion(predictions, label)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 训练模型
N_EPOCHS = 10
for epoch in range(N_EPOCHS):
    train_loss = train(model, train_loader, optimizer, criterion)
    print(f'Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f}')

在训练过程结束后，可以使用该模型对新的文本进行分类。具体方法是将文本转换为整数标记序列，然后使用模型进行预测：

# 对新文本进行分类
def predict(model, sentence):
    model.eval()
    tokenized = torch.tensor([vocab[token] for token in tokenizer(sentence)])
    length = torch.tensor([len(tokenized)])
    prediction = model(tokenized, length)
    return F.softmax(prediction, dim=1).detach().numpy()[0]

# 进行预测
test_sentence = "World markets are reacting to the news that the UK is set to leave the European Union."
pred_probs = predict(model, test_sentence)
print(pred_probs)

以上代码示例中使用了AG_NEWS数据集作为示例训练数据，可通过以下方式加载数据集：

from torchtext.datasets import AG_NEWS
train_iter = AG_NEWS(split='train')
test_iter = AG_NEWS(split='test')

该数据集包含四个类别的新闻数据，每个类别各有120,000个训练示例和7,600个测试示例。完整的训练脚本和数据集可以在PyTorch官方文档中找到。