Sagemaker - DistilBERT - Emotion: An open - source text sentiment classification model with an accuracy of 92.9% after fine

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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%

Text Classification

Transformers

Open Source License:Apache-2.0 #Sentiment Analysis #High Accuracy #Text Classification

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

Property	Details
Model Type	Fine - tuned version of distilbert-base-uncased
Training Data	emotion dataset
Metrics	Accuracy: 0.929
License	Apache - 2.0

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9345	1.0	500	0.2509	0.918
0.1855	2.0	1000	0.1626	0.928
0.1036	3.0	1500	0.1446	0.929

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Sagemaker Distilbert Emotion

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 sagemaker-distilbert-emotion

🚀 Quick Start

📚 Documentation

Model Information

Training Procedure

Training hyperparameters

Training results

Framework versions