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Distilbert Base Uncased Finetuned Emotion
A lightweight text sentiment classification model based on DistilBERT, fine-tuned on the emotion dataset with an accuracy of 92.4%
Downloads 17
Release Time : 3/2/2022
Model Overview
This model is a fine-tuned version of DistilBERT, specifically designed for text sentiment classification tasks, capable of identifying six basic emotions (anger, fear, joy, sadness, surprise, neutral)
Model Features
Efficient and Lightweight
Uses a distilled BERT architecture, reducing parameters by 40% while maintaining performance
High Accuracy
Achieves 92.4% accuracy and 0.924 F1 score on the emotion test set
Fast Inference
Approximately 60% faster inference speed compared to the original BERT model
Model Capabilities
Text Sentiment Classification
Emotion Recognition
Short Text Analysis
Use Cases
Social Media Analysis
User Comment Sentiment Monitoring
Automatically analyzes user sentiment tendencies in social media comments
Can identify over 92% of emotion labels in real-time
Customer Service
Customer Service Dialogue Analysis
Identifies emotional states in customer conversations to optimize service strategies
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