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Distilbert Base Uncased Finetuned Emotion

Developed by mattmcclean
A lightweight sentiment classification model based on DistilBERT, fine-tuned on emotion datasets with an accuracy of 92.5%
Downloads 17
Release Time : 3/2/2022

Model Overview

This model is a lightweight text classification model based on the DistilBERT architecture, specifically fine-tuned for sentiment analysis tasks. It can identify emotional tendencies in text and is suitable for various sentiment classification applications.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it is 40% smaller than the standard BERT model while retaining 95% of its performance
High Accuracy
Achieves 92.5% accuracy and 92.5% F1 score in sentiment classification tasks
Fast Inference
The distilled architecture design enables faster model inference, making it suitable for production environment deployment

Model Capabilities

Text sentiment classification
Emotional tendency analysis
Short text sentiment recognition

Use Cases

Social Media Analysis
User Comment Sentiment Analysis
Analyze the emotional tendencies of user comments on social media
Can accurately identify positive, negative, and neutral comments
Customer Service
Customer Feedback Classification
Automatically classify the emotional tendencies in customer feedback
Helps prioritize negative feedback to improve customer satisfaction
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