rubert-tiny2-russian-emotion-detection
| Property | Value |
|---|---|
| Parameter Count | 29.2M |
| License | MIT |
| Language | Russian |
| Accuracy | 76% (Single-label), 85% (Multi-label) |
| Framework | PyTorch |
What is rubert-tiny2-russian-emotion-detection?
This is a specialized BERT-based model designed for emotion detection in Russian text. It can classify text into seven distinct emotional categories: neutral, happiness, sadness, enthusiasm, fear, anger, and disgust. Developed by Aniemore, it represents a lightweight yet effective solution for Russian emotion analysis.
Implementation Details
The model is built on a tiny BERT architecture, optimized for Russian language processing. It utilizes the Transformers library and PyTorch framework, offering both single-label and multi-label classification capabilities. The model performs tokenization with a maximum sequence length of 512 tokens and employs softmax activation for probability distribution across emotion classes.
- Lightweight architecture with 29.2M parameters
- Support for both simple prediction and detailed emotion probability distribution
- Pre-trained on the CEDR M7 dataset
- Implements efficient inference with torch.no_grad()
Core Capabilities
- Seven-way emotion classification
- Multi-label classification with 85% accuracy
- Single-label classification with 76% accuracy
- Probability distribution output for all emotion categories
- Efficient processing of Russian text up to 512 tokens
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient architecture that balances size and performance, specifically optimized for Russian language emotion detection. Its ability to provide both single-label and probability distributions for emotions makes it versatile for various applications.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis in Russian social media monitoring, customer feedback analysis, and automated emotion tracking in Russian text. It's particularly suitable for applications requiring lightweight deployment while maintaining reasonable accuracy.
