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Few Shot Learning Classification Bert Sm 500

Developed by pravin691983
A text classification model trained with AutoTrain, suitable for few-shot learning scenarios, capable of efficiently classifying news articles.
Downloads 25
Release Time : 5/6/2024

Model Overview

This model is trained on the AG News dataset and can classify texts into four categories: World, Sports, Business, and Technology. Ideal for efficient news classification and management in media and content companies.

Model Features

Few-shot Learning
The model can be efficiently trained and classified with limited labeled data.
Efficient Classification
Capable of quickly and accurately classifying news articles into four main categories.
Diverse Applications
Suitable for various text classification scenarios, especially news content management.

Model Capabilities

Text Classification
Few-shot Learning
News Article Classification

Use Cases

Media and Content Management
News Classification
Automatically classify news articles to improve content management efficiency.
Classification accuracy reaches 88.25%
Personalized News Feed
Push classified news content based on user preferences.
Improves user satisfaction and engagement
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