Bert Gomotions

🚀 🔥 在GoEmotions数据集上微调的BERT模型

本模型是在GoEmotions数据集上对BERT进行微调，用于多标签情感分类。它能够为输入的文本预测多种情感。

🚀 快速开始

模型使用

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 加载模型和分词器
model_name = "codewithdark/bert-Gomotions"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 情感标签（根据你的数据集进行调整）
emotion_labels = [
    "Admiration", "Amusement", "Anger", "Annoyance", "Approval", "Caring", "Confusion",
    "Curiosity", "Desire", "Disappointment", "Disapproval", "Disgust", "Embarrassment",
    "Excitement", "Fear", "Gratitude", "Grief", "Joy", "Love", "Nervousness", "Optimism",
    "Pride", "Realization", "Relief", "Remorse", "Sadness", "Surprise", "Neutral"
]

# 示例文本
text = "I'm so happy today!"
inputs = tokenizer(text, return_tensors="pt")

# 预测
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits).squeeze(0)  # 将logits转换为概率

# 获取前5个预测结果
top5_indices = torch.argsort(probs, descending=True)[:5]  # 获取前5个标签的索引
top5_labels = [emotion_labels[i] for i in top5_indices]
top5_probs = [probs[i].item() for i in top5_indices]

# 打印结果
print("Top 5 Predicted Emotions:")
for label, prob in zip(top5_labels, top5_probs):
    print(f"{label}: {prob:.4f}")

'''
输出:
Top 5 Predicted Emotions:
Joy: 0.9478
Love: 0.7854
Optimism: 0.6342
Admiration: 0.5678
Excitement: 0.5231
'''

在Hugging Face中使用

from transformers import pipeline

classifier = pipeline("text-classification", model="codewithdark/bert-Gomotions", top_k=None)
classifier("I'm so excited about the trip!")

✨ 主要特性

• 这是一个微调版的BERT（bert-base-uncased）模型，在GoEmotions数据集上进行训练，可用于多标签情感分类，能为每个输入文本预测多种情感。

📊 性能

🔧 技术细节

• 模型：bert-base-uncased
• 数据集：GoEmotions
• 优化器：AdamW
• 损失函数：BCEWithLogitsLoss（用于多标签分类的二元交叉熵）
• 批量大小：16
• 训练轮数：3
• 评估指标：准确率、F1分数、汉明损失

📄 许可证

本模型使用的许可证为 apache-2.0。

🛠️ 引用

如果您使用了此模型，请引用以下内容：

@misc{your_model,
  author = {codewithdark},
  title = {Fine-tuned BERT on GoEmotions},
  year = {2025},
  url = {https://huggingface.co/codewithdark/bert-Gomotions}
}