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Distilbert Emotion

Developed by asimmetti
A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 94% accuracy on the evaluation set
Text Classification
Transformers
Open Source License:Apache-2.0#Sentiment Analysis#High Accuracy#Text Classification
Downloads 32
Release Time : 1/9/2025

Model Overview

This is a lightweight Transformer model optimized for text sentiment analysis tasks, capable of efficiently identifying emotional tendencies in text

Model Features

Efficient and Lightweight
Based on DistilBERT architecture, reducing model size by 40% while retaining 97% of the original BERT performance
High Accuracy
Achieves 94% classification accuracy on the evaluation set
Fast Inference
Distilled architecture design enables faster inference speed

Model Capabilities

Text Sentiment Analysis
Emotion Classification

Use Cases

Social Media Analysis
User Comment Sentiment Analysis
Analyze emotional tendencies in social media comments
Accurately identifies positive/negative sentiments
Customer Service
Customer Service Dialogue Analysis
Automatically identify emotional states in customer conversations
Helps prioritize customers with negative emotions
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