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# Emotion Detection Model - LongEmotion
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情感检测模型,基于BERT的中文情感分类器
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## 📊 模型信息
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- **基础模型**: bert-base-chinese
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- **任务类型**: 6分类情感检测
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- **验证准确率**: 91.47%
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- **框架**: PyTorch + Transformers
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## 🏷️ 情感类别
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模型可以识别以下6种情感:
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- `sadness` (悲伤)
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- `joy` (快乐)
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- `love` (爱)
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- `anger` (愤怒)
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- `fear` (恐惧)
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- `surprise` (惊讶)
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## 📁 文件说明
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```
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detection_hug/
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├── model.pt # 模型权重文件
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├── config.json # 模型配置
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├── tokenizer_config.json # 分词器配置
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├── vocab.txt # 词表
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├── special_tokens_map.json # 特殊符号映射
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├── detection_model.py # 模型定义
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├── inference_example.py # 推理示例
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└── README.md # 本文件
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```
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## 🚀 快速开始
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### 环境要求
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```bash
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pip install torch transformers
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```
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### 基本使用
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```python
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import torch
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from transformers import BertTokenizer
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from detection_model import EmotionDetectionModel
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# 1. 加载分词器
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tokenizer = BertTokenizer.from_pretrained(".")
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# 2. 加载模型
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model = EmotionDetectionModel(
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model_name="bert-base-chinese",
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num_emotions=6
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)
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checkpoint = torch.load("model.pt", map_location="cpu")
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model.load_state_dict(checkpoint)
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model.eval()
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# 3. 预测
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text = "我今天很开心!"
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encoding = tokenizer(text, return_tensors='pt', max_length=512, truncation=True, padding=True)
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outputs = model(**encoding)
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predicted_emotion = torch.argmax(outputs['logits'], dim=-1).item()
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# 情感映射
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emotions = ["sadness", "joy", "love", "anger", "fear", "surprise"]
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print(f"预测情感: {emotions[predicted_emotion]}")
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```
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### 使用推理脚本
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```bash
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python inference_example.py
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```
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## 📈 模型性能
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- **数据集**: dair-ai/emotion (中文情感数据)
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- **验证准确率**: 91.47%
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- **平均置信度**: 89.27%
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- **最大序列长度**: 512 tokens
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## 🔧 技术细节
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### 模型架构
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```
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EmotionDetectionModel
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├── BERT Encoder (bert-base-chinese)
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│ └── 768-dim hidden states
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├── Dropout (p=0.1)
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├── Linear Layer (768 → 384)
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├── ReLU Activation
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├── Dropout (p=0.1)
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└── Output Layer (384 → 6)
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```
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### 训练参数
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- **优化器**: AdamW
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- **学习率**: 2e-5
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- **批次大小**: 16
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- **最大长度**: 512
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## 📝 引用
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如果您使用此模型,请注明:
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```
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LongEmotion Detection Model
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- 基于 bert-base-chinese
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- 训练于 dair-ai/emotion 数据集
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```
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## 📧 联系方式
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如有问题,请通过项目仓库联系。
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---
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**License**: 遵循 bert-base-chinese 的许可协议
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**Created**: 2025
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