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Sagemaker Distilbert Emotion
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Sagemaker Distilbert Emotion

Developed by masapasa
A sentiment classification model based on DistilBERT, fine-tuned on sentiment datasets with an accuracy of 91.5%
Text Classification
Transformers
Open Source License:Apache-2.0#Sentiment Analysis#High Accuracy#Lightweight BERT
Downloads 15
Release Time : 3/2/2022

Model Overview

This model is a lightweight text classification model based on the DistilBERT architecture, specifically designed for sentiment analysis tasks. It achieves high classification accuracy through fine-tuning.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it is 40% smaller in size than the standard BERT model while retaining 97% of its performance.
High Accuracy
Achieves 91.5% accuracy in sentiment classification tasks.
Fast Inference
The distilled architecture design enables faster model inference.

Model Capabilities

Text Classification
Sentiment Analysis
Natural Language Processing

Use Cases

Sentiment Analysis
Social Media Sentiment Monitoring
Analyze user sentiment tendencies in social media posts
Can accurately identify positive/negative sentiments
Product Review Analysis
Automatically classify the sentiment of product reviews on e-commerce platforms
Accuracy rate of 91.5%
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