Fast, easy-to-use AI few-shot text classification, optimized for serverless functions.

EnergeticAI Classifiers

EnergeticAI Classifiers is a library for few-shot text classification, which is the task of classifying text into a set of categories, given only a few examples of each category.

There's no complicated training pipelines to maintain. Just provide a few examples of each category, and you're good to go.

It's great for assessing sentiment, categorizing support tickets, and more.

Install

Install this package, along with @energetic-ai/core, and any model weights (e.g. @energetic-ai/model-embeddings-en):

``bash
npm install @energetic-ai/core @energetic-ai/classifiers @energetic-ai/model-embeddings-en
`

Usage

You can easily call this method to create a classifier, and use it to predict categories for new text:

`js
import { initClassifier } from "@energetic-ai/classifiers";
import { modelSource } from "@energetic-ai/model-embeddings-en";

(async () => {
// Initialize with training examples
const classifier = await initClassifier(
[
["The world is happy", "Positive"],
["Work is so fun", "Positive"],
["I had a great day", "Positive"],
["I had a bad day", "Negative"],
["I am frustrated", "Negative"],
["I am depressed", "Negative"],
],
modelSource
);

// Classify a single string
const single = await classifier.classify("The weather is so nice today");
// { "label": "Positive" ... }

// Classify multiple strings in a batch
const multiple = await classifier.classify([
"What is this? I am so angry!",
"I am so excited!",
]);
// [{ "label": "Negative" ... }, { "label": "Positive" ... }]
})();
`

Examples

See the examples directory for examples.

Development

This repository uses Lerna to manage packages, and Vitest to run tests.

Run tests with this method:

`bash
npm run test
``

License

Apache 2.0, except for dependencies.

Acknowledgements

This project is derived from TensorFlow.js and the KNN Classifier library, which are also Apache 2.0 licensed.