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Distilbert Base Uncased Finetuned Emotion
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Distilbert Base Uncased Finetuned Emotion

Developed by jonc
A text classification model fine-tuned on sentiment datasets based on the DistilBERT base model, designed for sentiment analysis tasks.
Text Classification
Transformers
Open Source License:Apache-2.0#Sentiment Analysis#High Accuracy#Text Classification
Downloads 17
Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of DistilBERT, specifically designed for sentiment classification tasks. It performs exceptionally well on sentiment datasets, achieving an accuracy of 92.3%.

Model Features

Efficient and Lightweight
Based on the DistilBERT architecture, it is smaller and faster than standard BERT models while maintaining high performance.
High Accuracy
Achieves 92.3% accuracy and 92.3% F1 score on sentiment classification tasks.
Fast Training
Requires only 2 training epochs to achieve good performance, with high training efficiency.

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 92.3% of emotional expressions
Product Review Analysis
Automatically classify sentiment in product reviews on e-commerce platforms
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