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Distilbert Base Uncased Finetuned Emotion
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Distilbert Base Uncased Finetuned Emotion

Developed by Worldman
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#Sentiment Classification#High Accuracy#Text Analysis
Downloads 17
Release Time : 3/2/2022

Model Overview

A lightweight text classification model based on the DistilBERT architecture, specifically optimized for sentiment analysis tasks, capable of identifying emotional categories in text.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it reduces model size and computational resource requirements while maintaining high performance.
High Accuracy
Achieves 92.25% accuracy and 92.27% F1 score on the emotion dataset, demonstrating excellent performance.
Fast Training
Requires only 2 training epochs to achieve good performance, with high training efficiency.

Model Capabilities

Text Classification
Sentiment Analysis

Use Cases

Sentiment Analysis
Social Media Emotion Monitoring
Analyze user emotions in social media posts
Accurately identifies six basic emotion categories
Customer Feedback Analysis
Automatically classify sentiment tendencies in customer feedback
Helps businesses quickly understand customer satisfaction
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