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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 92.3%
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 emotional categories expressed in text.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is 40% smaller in size and 60% faster in inference compared to the original BERT
High Accuracy
Achieves 92.3% accuracy and F1 score on the emotion test set
Quick Fine-tuning
Requires only 2 training epochs to achieve excellent performance
Model Capabilities
Text Sentiment Classification
English Text Understanding
Short Text Analysis
Use Cases
Sentiment Analysis
Social Media Emotion Monitoring
Analyze user sentiment tendencies in social media posts
Can automatically classify into emotional categories such as anger, joy, sadness, etc.
Customer Feedback Analysis
Process sentiment tendencies in customer reviews and feedback
Identify positive/negative reviews and classify specific emotions
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