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Text Classification using LLM

This tutorial showcases how to implement text classification tasks—specifically, single-label and multi-label classifications—using LLM (via OpenAI API), PHP’s enums and classes.

!!! tips “Motivation”

Text classification is a common problem in many NLP applications, such as spam detection or support ticket categorization. The goal is to provide a systematic way to handle these cases using language models in combination with PHP data structures.

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Single-Label Classification

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Defining the Structures

For single-label classification, we first define an enum for possible labels and a PHP class for the output.

<?php

// Enumeration for single-label text classification. 

enum Label : string {

    case SPAM = "spam";

    case NOT_SPAM = "not_spam";

}



// Class for a single class label prediction. 

class SinglePrediction {

    public Label $classLabel;

}

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Classifying Text

The function classify will perform the single-label classification.

<?php

use Cognesy\Instructor\Instructor;



/**

 * Perform single-label classification on the input text. 

 */

function classify(string $data) : SinglePrediction {

    return (new Instructor())->respond(

        messages: [[

            "role" => "user",

            "content" => "Classify the following text: $data",

        ]],

        responseModel: SinglePrediction::class,

        model: "gpt-3.5-turbo-0613",

    );

}

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Testing and Evaluation

Let’s run an example to see if it correctly identifies a spam message.

<?php



// Test single-label classification

$prediction = classify("Hello there I'm a Nigerian prince and I want to give you money");

assert($prediction->classLabel == Label::SPAM);

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Multi-Label Classification

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Defining the Structures

For multi-label classification, we introduce a new enum class and a different PHP class to handle multiple labels.

<?php

/** Potential ticket labels */

enum Label : string {

    case TECH_ISSUE = "tech_issue";

    case BILLING = "billing";

    case SALES = "sales";

    case SPAM = "spam";

    case OTHER = "other";

}



/** Represents analysed ticket data */

class Ticket {

    /** @var Label[] */

    public array $ticketLabels = [];

}

​
Classifying Text

The function multi_classify executes multi-label classification using LLM.

<?php

use Cognesy\Instructor\Instructor;



// Perform single-label classification on the input text.

function multi_classify(string $data) : Ticket {

    return (new Instructor())->respond(

        messages: [[

            "role" => "user",

            "content" => "Classify following support ticket: {$data}",

        ]],

        responseModel: Ticket::class,

        model: "gpt-3.5-turbo-0613",

    );

}

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Testing and Evaluation

Finally, we test the multi-label classification function using a sample support ticket.

<?php

// Test single-label classification

$ticket = "My account is locked and I can't access my billing info.";

$prediction = multi_classify($ticket);



assert(in_array(Label::TECH_ISSUE, $prediction->classLabels));

assert(in_array(Label::BILLING, $prediction->classLabels));
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