Chinese Emotion Small

🚀 Chinese Text Emotion Classifier Small

A specialized model for Chinese text emotion analysis, fine - tuned from a pre - trained base model.

🚀 Quick Start

Install Dependencies

Ensure that you have installed Hugging Face's Transformers library and PyTorch:

pip install transformers torch

Load the Model

Use the following code to load the model and tokenizer, and perform emotion classification:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Add device settings
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Label mapping dictionary
label_mapping = {
    0: "Neutral tone",
    1: "Concerned tone",
    2: "Happy tone",
    3: "Angry tone",
    4: "Sad tone",
    5: "Questioning tone",
    6: "Surprised tone",
    7: "Disgusted tone"
}

def predict_emotion(text, model_path="Johnson8187/Chinese-Emotion-Small"):
    # Load the model and tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)  # Move the model to the device
    
    # Convert the text to the model input format
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)  # Move the input to the device
    
    # Make predictions
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Get the prediction result
    predicted_class = torch.argmax(outputs.logits).item()
    predicted_emotion = label_mapping[predicted_class]
    
    return predicted_emotion

if __name__ == "__main__":
    # Usage examples
    test_texts = [
        "Although I've tried hard for a long time, I always seem to fail. I feel useless.",
        "What you said really confuses me. I have no idea how to react.",
        "The world is so heartless. Why does it always give me such tests?",
        "Sometimes, I just hope to have a little peace and not hear these boring topics anymore.",
        "Every time I think of that past, my heart still aches. I really can't let it go.",
        "I never thought there would be such a big change. Now I feel completely out of control.",
        "I never expected you to do this. It surprises me beyond words.",
        "I know I should be stronger, but sometimes, these emotions really drive me crazy."
    ]

    for text in test_texts:
        emotion = predict_emotion(text)
        print(f"Text: {text}")
        print(f"Predicted emotion: {emotion}\n")

✨ Features

This model is fine - tuned based on the [MoritzLaurer/mDeBERTa - v3 - base - mnli - xnli](https://huggingface.co/MoritzLaurer/mDeBERTa - v3 - base - mnli - xnli) model, specializing in Chinese text emotion analysis. Through fine - tuning, the model can identify the following 8 emotion labels:

• Neutral tone
• Concerned tone
• Happy tone
• Angry tone
• Sad tone
• Questioning tone
• Surprised tone
• Disgusted tone

The model is applicable to various scenarios, such as customer service emotion monitoring, social media analysis, and user feedback classification.

📦 Dataset

• The fine - tuning dataset consists of 4,000 annotated Traditional Chinese emotion samples, covering various emotion categories to ensure the model's generalization capability in emotion classification.
• [Johnson8187/Chinese_Multi - Emotion_Dialogue_Dataset](https://huggingface.co/datasets/Johnson8187/Chinese_Multi - Emotion_Dialogue_Dataset)

📄 License

This project is under the MIT license.

🌟 Contact and Feedback

If you encounter any issues while using this model, please contact:

Email: fable8043@gmail.com Hugging Face Project Page: chinese-Emotion-Small