https://github.com/lambdacasserole/classr

Train microclassifiers in the cloud for spam detection, sentiment analysis and more.
https://github.com/lambdacasserole/classr

classification classification-model machine-learning mlaas naive-bayes naive-bayes-classifier saas t3-stack vercel

Last synced: about 1 month ago
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Train microclassifiers in the cloud for spam detection, sentiment analysis and more.

• Host: GitHub
• URL: https://github.com/lambdacasserole/classr
• Owner: lambdacasserole
• Created: 2022-12-24T16:18:20.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-27T15:42:03.000Z (11 months ago)
• Last Synced: 2024-11-01T09:28:38.875Z (3 months ago)
• Topics: classification, classification-model, machine-learning, mlaas, naive-bayes, naive-bayes-classifier, saas, t3-stack, vercel
• Language: HTML
• Homepage: https://classr.dev
• Size: 2.03 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classr

Train microclassifiers in the cloud for spam detection, sentiment analysis and more.

![Classr logo](./public/logo-light.svg)

## Overview

Classr is a web app that allows users to create *microclassifiers*. A microclassifier is just a machine learning model (classifier) that is trained using a minimal (but still reasonable) amount of training data (4MB max).

![Screenshot of app](./screenshot-app.png)

Classr uses the [bayes](https://www.npmjs.com/package/bayes) package at its core with custom additions for calculation of precision/recall/F1 score etc. and generation and rendering of confusion matrices.

## Why this project?

I wanted to see what I couuld do working on something from scratch with the T3 stack while sharpening my frontend skills in the process.

Turns out, it's a super cool stack that you should definitely try out if you haven't already!

* [create-t3-app](https://create.t3.gg/)

* [TypeScript](https://typescriptlang.org)

* [Next.js](https://nextjs.org)

* [NextAuth.js](https://next-auth.js.org)

* [Prisma](https://prisma.io)

* [Tailwind CSS](https://tailwindcss.com)

* [tRPC](https://trpc.io)

## Limitations

Classr trains multinomial naive bayes classifiers behind the scenes on unigrams generated by splitting strings along space characters. Train/test split is 20/80 and Laplace smoothing is at 1.

**Any ML engineer with tell you that this is a very simple classifier with a lot of caveats. Use appropriately, and wield responsibly.**

Classr does not support any of the following features (yet!):

* Training on n-grams.

* Tokenization based on anything but splitting documents along spaces.

* Stopword removal.

* More advanced classifiers (SVMs, random forests etc.)

## Deployment

This app is designed for deployment on [Vercel](https://create.t3.gg/en/deployment/vercel) but you might also be able to get it to work with [Docker](https://create.t3.gg/en/deployment/docker) (I haven't tried this myself yet).