Trias: A Fast and Smart Text Classifier

Trias is an innovative text classifier that empowers you to:

• Automatically infer categories from textual data.
• Train and update its model in real-time, allowing you to add training examples on the fly.
• Persist the trained model in a compact, portable, and compressed file.

Inspired by the ancient Greek prophetic nymphs known as the Trias (Cleodora, Melaina and Daphnis), this tool reimagines divination for the digital age. Just as the Trias interpreted omens to foresee the future, Trias processes large streams of text to generate insightful predictions.

Note: All Trias methods are asynchronous, so you can perform additional training and prediction simultaneously without blocking your application.

Installation

Install Trias via NPM:

npm install trias

Usage

Importing

import { Trias } from 'trias';

// or, using CommonJS:
const { Trias } = require('trias');

Initializing

Creating a new Trias instance.

const trias = new Trias({

    file: './nevermind.trias', // Path to the model file
    create: true, // Initialize a new model if none exists
    n: 3, // Maximum n-gram length (1-3)
    language: 'en', // ISO 2-letter code for the language (e.g., 'en' for English)
    excludes: ['separator', 'live', 'unknown'], // Terms to exclude
    size: 4096 * 1024 // Maximum model size in bytes

});

Training

Train Trias with text inputs and their corresponding categories asynchronously:

await trias.train("The future holds great promise.", "optimistic");
await trias.train("Dark clouds loom on the horizon.", "pessimistic");

// Train multiple texts with their associated categories.
// In this example, both texts will be linked to both "optimistic" and "utopic" categories.
await trias.train([
    "The future holds great promise.",
    "A rainbow looms on the horizon."
], ["optimistic", "utopic"]);

// Alternatively, use object notation:
await trias.train([{
    input: "Dark clouds loom on the horizon.",
    output: "pessimistic"
}]);

Predicting

Predict the category of a new text asynchronously:

const prediction = await trias.predict("What does the future hold?");
console.log(prediction);
// Example output: "optimistic"

For a more detailed prediction with scores:

const predictions = await trias.predict("What does the future hold?", { as: "objects", limit: 2 });
console.log(predictions);
// Example output:
// [
//   { category: "optimistic", score: 0.8 },
//   { category: "pessimistic", score: 0.2 }
// ]

For weighted predictions, pass an object where the keys are texts and the values are their weights:

const weightedPredictions = await trias.predict({
    "The future is bright": 2,
    "Challenges lie ahead": 1
});
console.log(weightedPredictions);

Saving

Save the instance's current state to the designated file:

await trias.save();

Resetting

Reset Trias by clearing all trained data:

trias.reset();

API Reference

Constructor Options

Methods

train(text, category)

Train the instance with a new example.

Parameters:

• text (string | object): The input text. You can also provide an object with an input property.
• category (string): The associated category or label for the text.

predict(text, options)

Asynchronously predicts the category of the input text using a TF-IDF approach.

Parameters:

• text (string | object): The input text for prediction.
• options (object):
  • as (string): Desired output format ('string', 'array', or 'objects').
  • limit (number): Maximum number of categories to return.

Returns: A prediction result in the specified format.

save()

Asynchronously saves the current state of the model to the designated file.

reset()

Clears all trained data and resets the model.

Notes

• Trias removes infrequently used omens to maintain the model within the specified size limit.