emotion-english
| Property | Value |
|---|---|
| Parameter Count | 82.1M |
| Model Type | Text Classification |
| Architecture | RoBERTa |
| License | MIT |
| Language | English |
What is emotion-english?
emotion-english is a sophisticated text classification model designed to detect and classify emotions across 20 distinct categories. Built by Jitesh, this model leverages the powerful RoBERTa architecture to provide nuanced emotional analysis of text inputs, from basic emotions like anger and joy to more complex states like empathetic and suspicious.
Implementation Details
The model is implemented using PyTorch and Transformers, utilizing Safetensors for efficient tensor operations. With 82.1M parameters, it offers a balanced trade-off between computational efficiency and accuracy. The model processes text input through a sequence classification pipeline, outputting both emotion labels and confidence scores.
- 20 distinct emotion classes including traditional (anger, joy, fear) and nuanced (cheeky, empathetic, suspicious) emotions
- Built on RoBERTa architecture for robust natural language understanding
- Easily integrable through Hugging Face's Transformers library
- Supports batch processing and real-time inference
Core Capabilities
- Fine-grained emotion detection across 20 categories
- Confidence scoring for emotion classifications
- Support for complex emotional states beyond basic emotions
- Optimized for English language text processing
- Production-ready with inference endpoints support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive coverage of 20 emotion categories, going beyond basic emotion detection to include subtle emotional states like 'cheeky', 'suspicious', and 'empathetic'. This granularity makes it particularly valuable for applications requiring detailed emotional analysis.
Q: What are the recommended use cases?
The model is ideal for social media sentiment analysis, customer feedback processing, content moderation, mental health applications, and any scenario requiring detailed emotional understanding of text. Its broad emotion spectrum makes it particularly suitable for applications requiring nuanced emotional intelligence.
