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Distilbert Base Uncased Finetuned Emotion
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Distilbert Base Uncased Finetuned Emotion

Developed by dmiller1
A text sentiment classification model based on DistilBERT, fine-tuned on the emotion dataset with an accuracy of 92.6%
Text Classification
Transformers
Open Source License:Apache-2.0#Sentiment Analysis#High Accuracy#Text Classification
Downloads 27
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. Fine-tuned on the emotion dataset, it can accurately identify emotional categories in text.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it significantly reduces model size while maintaining high performance.
High Accuracy
Achieves 92.6% accuracy and F1 score on the emotion test set.
Fast Inference
The distilled model offers faster inference speed compared to the original BERT model.

Model Capabilities

Text Sentiment Classification
Natural Language Understanding

Use Cases

Sentiment Analysis
Social Media Emotion Monitoring
Analyze user sentiment tendencies in social media text.
Accurately identifies six basic emotion categories.
Customer Feedback Analysis
Automatically classify sentiment tendencies in customer feedback.
Helps businesses quickly understand customer satisfaction.
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