用transformer进行sendiment-analysis代码

以下是使用Transformer进行情感分析的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.data import Field, TabularDataset, BucketIterator

# 设置随机种子
SEED = 1234
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True

# 定义字段
TEXT = Field(tokenize='spacy', lower=True)
LABEL = Field(sequential=False, dtype=torch.float)

# 加载数据集
train_data, test_data = TabularDataset.splits(
    path='path/to/data',
    train='train.csv',
    test='test.csv',
    format='csv',
    fields=[('text', TEXT), ('label', LABEL)]
)

# 构建词汇表
TEXT.build_vocab(train_data, max_size=10000, vectors='glove.6B.100d')
LABEL.build_vocab(train_data)

# 创建迭代器
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, test_iterator = BucketIterator.splits(
    (train_data, test_data),
    batch_size=BATCH_SIZE,
    device=device
)

# 定义Transformer模型
class Transformer(nn.Module):
    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim, n_layers, n_heads, pf_dim, dropout):
        super().__init__()
        self.embedding = nn.Embedding(input_dim, embedding_dim)
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(embedding_dim, n_heads, pf_dim, dropout),
            num_layers=n_layers
        )
        self.fc = nn.Linear(embedding_dim, hidden_dim)
        self.dropout = nn.Dropout(dropout)
        self.out = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, src):
        embedded = self.dropout(self.embedding(src))
        encoded = self.encoder(embedded)
        pooled = encoded.mean(dim=0)
        hidden = self.dropout(torch.relu(self.fc(pooled)))
        output = self.out(hidden)
        return output

# 初始化模型
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
HIDDEN_DIM = 256
OUTPUT_DIM = 1
N_LAYERS = 2
N_HEADS = 8
PF_DIM = 512
DROPOUT = 0.5

model = Transformer(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS, N_HEADS, PF_DIM, DROPOUT).to(device)

# 定义损失函数和优化器
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters())

# 定义训练和评估函数
def train(model, iterator, optimizer, criterion):
    model.train()
    epoch_loss = 0
    for batch in iterator:
        optimizer.zero_grad()
        predictions = model(batch.text).squeeze(1)
        loss = criterion(predictions, batch.label)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    return epoch_loss / len(iterator)

def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0
    with torch.no_grad():
        for batch in iterator:
            predictions = model(batch.text).squeeze(1)
            loss = criterion(predictions, batch.label)
            epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 训练模型
N_EPOCHS = 10
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
    train_loss = train(model, train_iterator, optimizer, criterion)
    valid_loss = evaluate(model, test_iterator, criterion)
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'sentiment_model.pt')

# 加载模型并进行预测
model.load_state_dict(torch.load('sentiment_model.pt'))
model.eval()

def predict_sentiment(sentence):
    model.eval()
    tokenized = [tok.text for tok in nlp.tokenizer(sentence)]
    indexed = [TEXT.vocab.stoi[t] for t in tokenized]
    tensor = torch.LongTensor(indexed).unsqueeze(1).to(device)
    prediction = torch.sigmoid(model(tensor))
    return prediction.item()

请确保将代码中的`path/to/data`替换为包含您数据集的路径。此外，您还需要下载预训练的GloVe词向量并将其放入正确的路径，以便在构建词汇表时使用。