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Distilbert Base Uncased Finetuned Emotion
A text sentiment classification model based on DistilBERT, fine-tuned on the emotion dataset with an accuracy of 92.25%
Downloads 26
Release Time : 3/2/2022
Model Overview
This model is a lightweight text classification model based on DistilBERT, specifically designed for sentiment analysis tasks. It performs exceptionally well when fine-tuned on the emotion dataset, making it suitable for applications requiring fast and accurate sentiment classification.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is 40% smaller than standard BERT while retaining 97% of its performance.
High Accuracy
Achieves 92.25% accuracy and 92.28% F1 score on the emotion test set.
Fast Inference
The distilled architecture design enables faster model inference, making it suitable for production environment deployment.
Model Capabilities
Text sentiment classification
Natural language processing
Sentiment analysis
Use Cases
Social media analysis
User comment sentiment analysis
Analyze the sentiment tendencies in social media comments
Can accurately identify over 92% of sentiment categories
Customer service
Customer feedback classification
Automatically classify the emotional state in customer feedback
Helps quickly identify dissatisfied customers and prioritize their issues
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