Indonesian RoBERTa Base Emotion Classifier
| Property | Value |
|---|---|
| Parameter Count | 125M |
| License | MIT |
| Framework | PyTorch, Transformers |
| Dataset | IndoNLU EmoT |
| Best Performance | F1-score: 72.05%, Accuracy: 71.81% |
What is indonesian-roberta-base-emotion-classifier?
This model is a specialized emotion classifier built on the Indo-RoBERTa base architecture, specifically designed for Indonesian text analysis. Developed by Steven Limcorn, it represents a significant advancement in Indonesian natural language processing, utilizing transfer learning to achieve high-accuracy emotion classification.
Implementation Details
The model was trained through a careful process of transfer learning, using the Indo-roberta base model as its foundation. The training process spanned 7 epochs with a learning rate of 2e-5, resulting in continuously improving performance metrics. The implementation uses the Transformers library and can be easily integrated into existing NLP pipelines.
- Trained on IndoNLU EmoT dataset
- Achieves 72.47% precision and 71.94% recall
- Implements advanced transfer learning techniques
- Optimized for Indonesian language processing
Core Capabilities
- Emotion classification for Indonesian text
- Easy integration through Transformers pipeline
- Robust performance across various emotional contexts
- Production-ready with safetensors support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Indonesian emotion classification, built on the robust RoBERTa architecture and achieving state-of-the-art performance metrics for Indonesian language emotion analysis.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis in Indonesian text, social media monitoring, customer feedback analysis, and any application requiring emotional context understanding in Indonesian language content.
