细粒度情感分析pytorch
时间: 2023-10-18 13:32:05 浏览: 145
细粒度情感分析(Fine-grained Sentiment Analysis)是指将文本的情感进行更细致的划分,而不仅仅是分为正面、负面和中性三类。PyTorch是一个深度学习框架,可以用于构建和训练情感分析模型。下面是一个基于PyTorch的细粒度情感分析的简单示例代码:
首先,我们需要准备数据集。可以使用一些带有情感标签的文本数据集,如IMDB电影评论数据集。
```
import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.legacy import data
from torchtext.legacy import datasets
# 定义处理文本的Field对象
TEXT = data.Field(tokenize='spacy', lower=True)
LABEL = data.LabelField(dtype=torch.float)
# 下载和加载IMDB电影评论数据集
train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)
# 构建词汇表
TEXT.build_vocab(train_data, max_size=25000, vectors='glove.6B.100d', unk_init=torch.Tensor.normal_)
LABEL.build_vocab(train_data)
# 创建迭代器
train_iterator, test_iterator = data.BucketIterator.splits((train_data, test_data), batch_size=64)
```
接下来,我们可以定义一个简单的双向LSTM模型:
```
class SentimentLSTM(nn.Module):
def __init__(self, embedding_dim, hidden_dim, output_dim, num_layers, bidirectional, dropout):
super().__init__()
self.embedding = nn.Embedding(len(TEXT.vocab), embedding_dim)
self.lstm = 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):
embedded = self.dropout(self.embedding(text))
output, (hidden, cell) = self.lstm(embedded)
hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1))
return self.fc(hidden.squeeze(0))
```
然后,我们可以定义训练和评估的函数:
```
def train(model, iterator, optimizer, criterion):
model.train()
for batch in iterator:
optimizer.zero_grad()
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
loss.backward()
optimizer.step()
def evaluate(model, iterator, criterion):
model.eval()
total_loss = 0
total_correct = 0
with torch.no_grad():
for batch in iterator:
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
total_loss += loss.item()
predicted_labels = torch.round(torch.sigmoid(predictions))
total_correct += (predicted_labels == batch.label).sum().item()
return total_loss / len(iterator), total_correct / len(iterator.dataset)
```
最后,我们可以定义模型的超参数,初始化模型并进行训练和评估:
```
embedding_dim = 100
hidden_dim = 256
output_dim = 1
num_layers = 2
bidirectional = True
dropout = 0.5
model = SentimentLSTM(embedding_dim, hidden_dim, output_dim, num_layers, bidirectional, dropout)
optimizer = optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
criterion = criterion.to(device)
num_epochs = 10
for epoch in range(num_epochs):
train(model, train_iterator, optimizer, criterion)
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print(f'Epoch: {epoch+1}, Test Loss: {test_loss:.4f}, Test Accuracy: {test_acc:.4f}')
```
这只是一个简单的示例,你可以根据自己的需求对模型进行更进一步的调整和改进。希望对你有帮助!
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