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Sagemaker Distilbert Emotion
S
Sagemaker Distilbert Emotion
Developed by anindabitm
A sentiment analysis model based on DistilBERT, fine-tuned on sentiment datasets with an accuracy of 91.65%
Downloads 18
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. Through fine-tuning, it can effectively identify sentiment tendencies in text.
Model Features
High Accuracy
Achieves 91.65% accuracy on the evaluation set, demonstrating excellent performance
Lightweight Architecture
Based on DistilBERT, it is smaller and faster than standard BERT models
Efficient Training
Uses mixed-precision training and linear learning rate scheduling for high training efficiency
Model Capabilities
Text Classification
Sentiment Analysis
English Text Processing
Use Cases
Sentiment Analysis
Social Media Sentiment Monitoring
Analyze user sentiment tendencies in social media posts
Can accurately identify positive, negative, and other sentiments
Product Review Analysis
Automatically classify sentiment in product reviews on e-commerce platforms
Accuracy as high as 91.65%
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