twitter-roberta-base-emotion-multilabel-latest
| Property | Value |
|---|---|
| Base Model | cardiffnlp/twitter-roberta-base-2022-154m |
| Task | Multilabel Emotion Classification |
| Performance | F1 (micro): 0.7169, F1 (macro): 0.5464 |
| Downloads | 4,603 |
What is twitter-roberta-base-emotion-multilabel-latest?
This is a specialized emotion classification model fine-tuned on the SemEval 2018 Task 1 Affect in Tweets dataset. Built upon RoBERTa architecture, it's specifically designed to detect multiple emotions simultaneously in social media text, making it particularly effective for Twitter content analysis.
Implementation Details
The model leverages the robust RoBERTa architecture and can be easily implemented using either the tweetnlp package or the Transformers pipeline. It supports multilabel classification across various emotions including anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, and trust.
- Built on twitter-roberta-base-2022-154m architecture
- Achieves 0.5970 Jaccard Index on samples
- Supports both tweetnlp and Transformers pipeline implementation
Core Capabilities
- Multilabel emotion detection
- Real-time Twitter text analysis
- Confidence scores for each emotion category
- Support for 11 distinct emotion categories
Frequently Asked Questions
Q: What makes this model unique?
This model's ability to detect multiple emotions simultaneously in social media text, combined with its specialized training on Twitter data, makes it particularly effective for social media sentiment analysis. Its high F1 scores demonstrate robust performance across various emotion categories.
Q: What are the recommended use cases?
The model is ideal for social media monitoring, sentiment analysis, emotion tracking in customer feedback, and research applications requiring detailed emotional analysis of text. It's particularly suited for applications requiring nuanced understanding of multiple concurrent emotions.
