emotion_text_classifier
| Property | Value |
|---|---|
| Author | michellejieli |
| Downloads | 887,929 |
| Base Architecture | DistilRoBERTa |
| Task | Emotion Classification |
What is emotion_text_classifier?
emotion_text_classifier is a specialized emotion detection model built on DistilRoBERTa-base architecture, fine-tuned specifically on Friends TV show transcripts. The model is designed to classify text into seven distinct emotional categories: anger, disgust, fear, joy, neutrality, sadness, and surprise. It leverages the Ekman emotion framework while maintaining high accuracy through its transformer-based architecture.
Implementation Details
The model is implemented using the Transformers library and PyTorch backend. It builds upon the emotion-english-distilroberta-base model and has been further fine-tuned on the Emotion Lines dataset from Friends. The model was initially trained on multiple emotion datasets including Crowdflower, GoEmotions, ISEAR, and MELD.
- Transformer-based architecture using DistilRoBERTa
- Seven-class classification capability
- Simple integration with Hugging Face's pipeline API
- Optimized for dialogue and conversational text
Core Capabilities
- Multi-class emotion detection across 7 categories
- Specialized in processing conversational text and dialogue
- High-performance sentiment analysis
- Easy integration with Python applications
- Support for batch processing
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its specialized training on TV show dialogue, particularly Friends transcripts, making it especially effective for analyzing conversational text and informal communication. Its combination of multiple emotion datasets with TV show data provides a robust foundation for emotion detection.
Q: What are the recommended use cases?
The model is particularly well-suited for analyzing social media content, chat conversations, customer service interactions, and entertainment content. It excels in scenarios where understanding emotional context in informal communication is crucial.
