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Sagemaker Roberta Base Emotion

Developed by Jorgeutd
A multi-class text classification model fine-tuned based on the RoBERTa-base architecture, specifically designed for emotion detection tasks, achieving 94.1% accuracy on the emotion dataset.
Downloads 41
Release Time : 3/2/2022

Model Overview

This model is fine-tuned using Amazon SageMaker and Hugging Face deep learning containers for detecting emotion categories in text, supporting high-precision multi-classification tasks.

Model Features

High-precision emotion recognition
Achieves 93.1% accuracy and 94.13 F1 score on the emotion test set.
SageMaker optimization
Optimized for training via the Amazon SageMaker platform, supporting efficient inference.
Robust text processing
Based on the RoBERTa-base architecture, excels at handling complex text semantics.

Model Capabilities

Text emotion classification
Multi-class prediction
Natural language understanding

Use Cases

Sentiment analysis
Customer feedback analysis
Automatically analyzes emotional tendencies in customer reviews.
Accurately identifies negative emotions such as frustration/anger (example input verified).
Social media monitoring
Real-time monitoring of emotional distribution in social media posts.
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