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Distilbert Base Uncased Finetuned Emotion
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Distilbert Base Uncased Finetuned Emotion

Developed by carlosaguayo
A lightweight sentiment analysis model based on DistilBERT, fine-tuned on emotion datasets with an accuracy of 92.95%
Text Classification
Transformers
Open Source License:Apache-2.0#Sentiment Analysis#High Accuracy#Text Classification
Downloads 20
Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of DistilBERT, specifically designed for text sentiment classification tasks. It retains the main performance of BERT through distillation technology while reducing model size and computational requirements.

Model Features

Efficient and Lightweight
Utilizes distillation technology, making the model 40% smaller than standard BERT and 60% faster in inference while retaining 95% of the performance.
High Accuracy
Achieves 92.95% accuracy and 93.0% F1 score in sentiment classification tasks.
Fast Fine-Tuning
Requires only 2 training epochs to achieve excellent performance, with training loss decreasing from 0.2853 to 0.1568.

Model Capabilities

Text Sentiment Analysis
Short Text Classification
Sentiment Polarity Judgment

Use Cases

Social Media Analysis
User Comment Sentiment Analysis
Analyze the sentiment tendencies of social media comments
Accurately identifies 92.95% of comment sentiments
Customer Service
Customer Feedback Classification
Automatically classify the sentiment tendencies of customer feedback
Helps quickly identify negative feedback
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