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情感检测模型
Emotion Detection Task
多标签分类任务
"""
import torch
import torch.nn as nn
from transformers import (
BertModel,
BertTokenizer,
AutoModel,
AutoTokenizer
)
from typing import Dict, List, Optional
class EmotionDetectionModel(nn.Module):
"""情感检测模型（多标签分类）"""
def __init__(
self,
model_name: str = "bert-base-chinese",
num_emotions: int = 7,
dropout: float = 0.1
):
"""
初始化检测模型
Args:
model_name: 预训练模型名称
num_emotions: 情感类别数量
dropout: Dropout 比率
"""
super().__init__()
self.bert = AutoModel.from_pretrained(model_name)
hidden_size = self.bert.config.hidden_size
# 多标签分类头
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(hidden_size, hidden_size // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_size // 2, num_emotions)
)
self.num_emotions = num_emotions
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
labels: Optional[torch.Tensor] = None
):
"""
前向传播
Args:
input_ids: 输入ID
attention_mask: 注意力掩码
labels: 标签（多标签，形状为 [batch_size, num_emotions]）
Returns:
模型输出
"""
outputs = self.bert(
input_ids=input_ids,
attention_mask=attention_mask
)
# 使用 [CLS] token 的表示
pooled_output = outputs.last_hidden_state[:, 0, :]
# 分类
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
# 使用 BCE Loss 进行多标签分类
loss_fct = nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels.float())
return {
'loss': loss,
'logits': logits
}
class EmotionDetectionModelWrapper:
"""情感检测模型封装器"""
def __init__(
self,
model_name: str = "bert-base-chinese",
num_emotions: int = 7,
max_length: int = 512,
device: str = "cuda" if torch.cuda.is_available() else "cpu",
threshold: float = 0.5
):
"""
初始化
Args:
model_name: 预训练模型名称
num_emotions: 情感类别数量
max_length: 最大序列长度
device: 设备
threshold: 分类阈值
"""
self.model_name = model_name
self.num_emotions = num_emotions
self.max_length = max_length
self.device = device
self.threshold = threshold
# 加载分词器
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
# 加载模型
self.model = EmotionDetectionModel(
model_name=model_name,
num_emotions=num_emotions
).to(device)
# 情感标签名称
self.emotion_names = [
"happiness",
"sadness",
"anger",
"fear",
"surprise",
"disgust",
"neutral"
]
def preprocess(self, texts: List[str]) -> Dict[str, torch.Tensor]:
"""预处理文本"""
encoding = self.tokenizer(
texts,
padding=True,
truncation=True,
max_length=self.max_length,
return_tensors="pt"
)
return {k: v.to(self.device) for k, v in encoding.items()}
def predict(self, texts: List[str]) -> List[Dict[str, any]]:
"""
批量预测
Args:
texts: 文本列表
Returns:
预测结果列表
"""
self.model.eval()
inputs = self.preprocess(texts)
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs['logits']
probabilities = torch.sigmoid(logits) # 多标签使用 sigmoid
results = []
for i, probs in enumerate(probabilities):
# 获取超过阈值的情感
detected_emotions = []
emotion_scores = {}
for j, prob in enumerate(probs):
emotion_scores[self.emotion_names[j]] = float(prob)
if prob >= self.threshold:
detected_emotions.append({
'emotion': self.emotion_names[j],
'score': float(prob)
})
results.append({
'text': texts[i],
'emotions': detected_emotions,
'all_scores': emotion_scores
})
return results
def save(self, save_path: str):
"""保存模型"""
import os
os.makedirs(save_path, exist_ok=True)
# 保存模型权重
torch.save(self.model.state_dict(), f"{save_path}/model.pt")
# 保存分词器
self.tokenizer.save_pretrained(save_path)
# 保存配置
import json
config = {
'model_name': self.model_name,
'num_emotions': self.num_emotions,
'max_length': self.max_length,
'threshold': self.threshold
}
with open(f"{save_path}/config.json", 'w') as f:
json.dump(config, f, indent=2)
print(f"检测模型已保存到 {save_path}")
@classmethod
def load(cls, model_path: str, device: str = "cuda"):
"""加载模型"""
import json
# 加载配置
with open(f"{model_path}/config.json", 'r') as f:
config = json.load(f)
instance = cls(
model_name=config['model_name'],
num_emotions=config['num_emotions'],
max_length=config['max_length'],
device=device,
threshold=config.get('threshold', 0.5)
)
# 加载权重
instance.model.load_state_dict(
torch.load(f"{model_path}/model.pt", map_location=device)
)
print(f"检测模型已从 {model_path} 加载")
return instance
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