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Sagemaker Distilbert Emotion

Developed by jpabbuehl
A text sentiment classification model based on DistilBERT, fine-tuned on the emotion dataset with an accuracy of 92.9%
Downloads 21
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 recognizing six basic emotions.

Model Features

High Accuracy
Achieves 92.9% classification accuracy on the emotion dataset
Lightweight
Based on the DistilBERT architecture, more lightweight and efficient than the full BERT model
Fast Inference
Optimized model suitable for production environment deployment

Model Capabilities

Text Classification
Sentiment Analysis

Use Cases

Social Media Analysis
User Comment Sentiment Analysis
Analyze user emotions in social media comments
Accurately identifies six basic emotions including anger, joy, sadness, etc.
Customer Service
Customer Feedback Classification
Automatically classify emotional tendencies in customer feedback
Helps quickly identify dissatisfied customers
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