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.
Single-Label Classification
Defining the Structures
For single-label classification, we first define an enum for possible labels and a PHP class for the output.
<?php
enum Label : string {
case SPAM = "spam";
case NOT_SPAM = "not_spam";
}
class SinglePrediction {
public Label $classLabel;
}
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",
);
}
Testing and Evaluation
Let’s run an example to see if it correctly identifies a spam message.
<?php
$prediction = classify("Hello there I'm a Nigerian prince and I want to give you money");
assert($prediction->classLabel == Label::SPAM);
Multi-Label Classification
Defining the Structures
For multi-label classification, we introduce a new enum class and a different PHP class to handle multiple labels.
<?php
enum Label : string {
case TECH_ISSUE = "tech_issue";
case BILLING = "billing";
case SALES = "sales";
case SPAM = "spam";
case OTHER = "other";
}
class Ticket {
public array $ticketLabels = [];
}
Classifying Text
The function multi_classify executes multi-label classification using LLM.
<?php
use Cognesy\Instructor\Instructor;
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",
);
}
Testing and Evaluation
Finally, we test the multi-label classification function using a sample support ticket.
<?php
$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));
