bund_stdlib_text_classifier

Crates.io	bund_stdlib_text_classifier
lib.rs	bund_stdlib_text_classifier
version	0.5.0
created_at	2025-09-10 14:18:06.579844+00
updated_at	2025-09-11 06:29:19.476515+00
description	Text classification using Bayes classifier for BUND programming language
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repository	https://github.com/vulogov/bund_stdlib_text_classifier
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id	1832568
size	158,563

Vladimir Uogov (vulogov)

documentation

README

Module for BUND standard library: Naive Bayes text classifier

A powerful and flexible text classification module built on top of the bund machine learning framework. This module is part of the Bund Standard Library (stdlib) and provides a streamlined workflow for training, evaluating, and deploying text classification models. This module is a library and not designed to be a standalone application, but it can be embedded inside BUND virtual machine.

Installation

This module required make and Rust framework to be installed first. After that:

cargo add bund_stdlib_text_classifier

Quick start

Get started with a simple example to classify text data. First, you have to create and train classifier

:TEST textclassifier.new
    :rust "./scripts/rust.txt" textclassifier.train.from_file
    "{A} tokens for RUST" format println
    :kant "./scripts/kant.txt" textclassifier.train.from_file
    "{A} tokens for KANT" format println
    :astronomy "./scripts/astronomy.txt" textclassifier.train.from_file
    "{A} tokens for ASTRONOMY" format println
    :tolstoy "./scripts/tolstoy.txt" textclassifier.train.from_file
    "{A} tokens for LEO TOLSTOY" format println
    textclassifier.train.finish

Then you can classify any text lines.

:TEST
  "At its simplest, a test in Rust is a function that’s annotated with the test attribute. Attributes are metadata about pieces of Rust code"
    textclassifier.classify

The following call will return a DICT value:

{
  "astronomy": 0.8331765363980779,
  "kant": 0.9968812285706273,
  "rust": 1.0,
  "tolstoy": 0.9968812285706273
}

BUND functions exposed in this module

Name	Stack IN	Stack OUT	Description
textclassifier.new	`Classifier name`	`Classifier name`	Create new classifier
textclassifier.exists	`Classifier name`	`Classifier name` `TRUE/FALSE`	Check if classifier exists
textclassifier.train.from_file	`Classifier name` `Category` `Filename`	`Classifier name` `Number of tokens`	Train classifier from text file
textclassifier.train.finish	`Classifier name`	`Classifier name`	Finalize classifier training
textclassifier.classify	`Classifier name` `Text for classification`	`Classifier name` `DICT with scores`	Classify text string using pre-trained classifier

Commit count: 24