Chinese-Emotion-Small
| Property | Value |
|---|---|
| Base Model | mDeBERTa-v3-base-mnli-xnli |
| Training Dataset | 4,000 annotated Traditional Chinese samples |
| Author | Johnson8187 |
| Model Hub | Hugging Face |
What is Chinese-Emotion-Small?
Chinese-Emotion-Small is a specialized emotion classification model fine-tuned from mDeBERTa-v3-base-mnli-xnli, designed specifically for Chinese text emotion analysis. This lightweight version offers efficient emotion detection across 8 distinct emotional categories, making it ideal for production deployments where resource optimization is crucial.
Implementation Details
The model leverages the robust architecture of mDeBERTa-v3 and has been fine-tuned on a carefully curated dataset of 4,000 annotated Traditional Chinese emotion samples. It implements a sophisticated classification system capable of detecting neutral, concerned, happy, angry, sad, questioning, surprised, and disgusted tones in text.
- Built on advanced transformer architecture
- Optimized for Traditional Chinese text processing
- Implements PyTorch-based inference
- Supports both CPU and CUDA acceleration
Core Capabilities
- Accurate classification of 8 distinct emotional tones
- Efficient processing of Chinese text input
- Real-time emotion analysis capability
- Suitable for customer service monitoring
- Applicable for social media sentiment analysis
- Effective in user feedback classification
Frequently Asked Questions
Q: What makes this model unique?
The model's specialization in Chinese text emotion analysis, combined with its ability to distinguish between 8 different emotional tones, makes it particularly valuable for applications requiring nuanced emotional understanding in Chinese language contexts. Its lightweight architecture ensures efficient deployment while maintaining high accuracy.
Q: What are the recommended use cases?
The model is ideally suited for customer service emotion monitoring, social media sentiment analysis, and user feedback classification. It can be effectively integrated into applications requiring real-time emotion detection in Chinese text, such as chatbots, customer support systems, and social media monitoring tools.
