Covid-Tweet Classification Open-Source Model - Free Deployment to Assist in Analyzing COVID-Related Tweet Content

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Covid Tweet Classification

Developed by atrisaxena

A fine-tuned DistilBERT-based model for classifying COVID-related tweets

Text Classification

Transformers

Open Source License:Apache-2.0 #COVID Tweet Classification #Lightweight BERT #Short Text Analysis

Downloads 20

Release Time : 9/19/2023

Model Overview

This model is a text classification model fine-tuned on COVID-related datasets based on distilbert-base-uncased, primarily used for analyzing and classifying tweet content

Model Features

Efficient and Lightweight

Based on DistilBERT architecture, reducing model size while maintaining performance

High Accuracy

Achieves 92.82% classification accuracy on the training set

Fast Training

Requires only 4 training epochs to achieve good performance

Model Capabilities

Text Classification

Tweet Content Analysis

COVID-related Content Identification

Use Cases

Social Media Analysis

COVID-related Tweet Classification

Classify and analyze COVID-related tweets on social media

Can identify tweet content categories with 92.82% accuracy

Public Health Monitoring

Epidemic-related Public Sentiment Analysis

Monitor and analyze public discussions and sentiments about COVID

Train Loss	Validation Loss	Train Accuracy	Epoch
0.0357	0.2751	0.9282	0
0.0194	0.2786	0.9282	1
0.0162	0.2786	0.9282	2
0.0167	0.2786	0.9282	3
0.0168	0.2786	0.9282	4

Property	Details
Transformers	4.33.2
TensorFlow	2.13.0
Datasets	2.14.5
Tokenizers	0.13.3

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Covid Tweet Classification

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 atrisaxena/covid-tweet-classification

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📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

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