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Distilbert Finetuned On Emotion
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Distilbert Finetuned On Emotion
Developed by Rahmat82
A text classification model fine-tuned on emotion datasets based on DistilBERT, used to identify emotional categories in text.
Downloads 1,652
Release Time : 12/3/2023
Model Overview
This model is a fine-tuned version of the DistilBERT architecture on the emotion dataset, specifically designed for text sentiment classification tasks.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it reduces model size and computational resource requirements while maintaining high performance.
High Accuracy
Achieves 92.35% accuracy and F1 score in sentiment classification tasks.
Fast Inference
Due to the DistilBERT architecture, the model has fast inference speed, making it suitable for real-time applications.
Model Capabilities
Text Sentiment Classification
Natural Language Processing
Use Cases
Sentiment Analysis
Social Media Sentiment Monitoring
Analyze user sentiment tendencies in social media posts
Can accurately identify emotions such as happiness, sadness, and anger
Customer Feedback Analysis
Automatically classify sentiment tendencies in customer feedback
Helps businesses quickly understand customer satisfaction
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