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News Category Classifier

Developed by ranudee
A lightweight text classification model based on the DistilBERT architecture, designed to classify English news into four major categories: World, Sports, Business, and Technology
Downloads 526
Release Time : 11/20/2024

Model Overview

This model is a fine-tuned variant of DistilBERT on the AG News dataset, specifically designed for four-class classification tasks of English news articles. As a lightweight version of BERT, it improves inference speed while maintaining high accuracy.

Model Features

Lightweight and Efficient
Utilizes the DistilBERT architecture, reducing model size by 40% compared to the original BERT while retaining 97% of its performance
Multi-classification Capability
Accurately identifies news articles belonging to four major categories (World/Sports/Business/Technology)
Real-time Inference
Optimized architecture suitable for production environments requiring fast response times

Model Capabilities

English text classification
News content analysis
Real-time text processing

Use Cases

Media Analysis
Automated News Classification System
Automatically tags articles for news aggregation platforms
Achieves 90%+ classification accuracy (based on inference data)
Business Intelligence
Industry Trend Monitoring
Identifies business and technology-related content from news streams
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