EmoRoBERTa
| Property | Value |
|---|---|
| Author | arpanghoshal |
| Model Type | Emotion Classification |
| Base Architecture | RoBERTa |
| Training Data | 58,000 Reddit comments |
| Performance | 49.30% Macro F1 |
What is EmoRoBERTa?
EmoRoBERTa is an advanced emotion detection model built on the RoBERTa architecture, specifically designed to classify text into 28 distinct emotional categories. This model represents a significant advancement in emotional text analysis, trained on a diverse dataset of 58,000 Reddit comments to capture nuanced emotional expressions.
Implementation Details
The model utilizes the RoBERTa architecture with carefully tuned hyperparameters including a learning rate of 5e-5, 10 training epochs, and a maximum sequence length of 50 tokens. Training was conducted with a batch size of 16 and incorporates a warmup proportion of 0.1.
- 28 emotion categories including admiration, amusement, anger, annoyance, and more
- Built on RoBERTa's enhanced language masking strategy
- Optimized for accurate emotion classification in social media text
- Simple integration with the Transformers library
Core Capabilities
- Fine-grained emotion classification across 28 categories
- Handles social media text effectively
- High accuracy for common emotions (demonstrated by 0.99+ confidence scores)
- Easy integration with Python applications
Frequently Asked Questions
Q: What makes this model unique?
EmoRoBERTa stands out for its comprehensive emotion classification capabilities, covering 28 distinct emotions plus neutral state, making it one of the most granular emotion detection models available. Its training on Reddit comments makes it particularly effective for social media content analysis.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis in social media monitoring, customer feedback analysis, and any application requiring detailed emotional understanding of text. It's particularly effective for applications requiring fine-grained emotion detection beyond simple positive/negative sentiment.
