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Distilbert Base Uncased Finetuned Emotion
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Distilbert Base Uncased Finetuned Emotion

Developed by FabioDataGeek
This model is a text classification model fine-tuned on the emotion dataset based on DistilBERT, designed for sentiment analysis tasks.
Text Classification
Transformers
Open Source License:Apache-2.0#Emotion Classification#High Accuracy#Lightweight BERT
Downloads 19
Release Time : 3/2/2022

Model Overview

This model is a lightweight pre-trained model based on the DistilBERT architecture, fine-tuned on the emotion dataset, specifically designed for text emotion classification tasks.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it is more lightweight and efficient than standard BERT models while maintaining high performance.
High Accuracy
Achieves 92.6% accuracy and 92.58% F1 score on the emotion dataset.
Fast Training
Requires only 2 epochs of training to achieve excellent performance, with high training efficiency.

Model Capabilities

Text Classification
Sentiment Analysis

Use Cases

Sentiment Analysis
Social Media Sentiment Monitoring
Analyze sentiment tendencies in social media texts
Can accurately identify emotion categories in texts
Customer Feedback Analysis
Automatically classify sentiment tendencies in customer feedback
Helps businesses quickly understand customer emotions
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