distilbert-base-turkish-cased-emotion
| Property | Value |
|---|---|
| Author | zafercavdar |
| Model Type | Emotion Classification |
| Accuracy | 83.25% |
| F1 Score | 83.17% |
| Base Architecture | DistilBERT |
What is distilbert-base-turkish-cased-emotion?
This is a specialized emotion classification model built on DistilBERT architecture, specifically fine-tuned for Turkish language text. The model can identify six distinct emotions: joy, sadness, love, anger, fear, and surprise. It achieves impressive performance metrics with 83.25% accuracy and 83.17% F1 score on the test set.
Implementation Details
The model was developed by fine-tuning distilbert-base-turkish-cased on an emotion dataset translated to Turkish using Google Translate API. The training process utilized HuggingFace Trainer with carefully selected hyperparameters: learning rate of 2e-5, batch size of 64, and 8 training epochs. The model processes 232.197 test samples per second, making it efficient for real-world applications.
- Built on Turkish-cased DistilBERT base model
- Fine-tuned on translated Twitter emotion dataset
- Optimized for performance and efficiency
- Easy integration with HuggingFace pipeline
Core Capabilities
- Multi-class emotion classification for Turkish text
- Handles six distinct emotional categories
- Provides confidence scores for each emotion
- High-speed inference capability
- Production-ready performance metrics
Frequently Asked Questions
Q: What makes this model unique?
This model stands out as a specialized emotion classifier for Turkish text, offering high accuracy while maintaining efficient processing speeds. Its ability to handle multiple emotion categories with balanced performance makes it particularly valuable for Turkish NLP applications.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis in Turkish social media content, customer feedback analysis, emotional content monitoring, and any application requiring Turkish text emotion classification. It's particularly suited for applications requiring real-time analysis due to its high processing speed.
