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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Zero Shot Classification - Hugging Face Transformers.js</title>
<script type="module">
// To-Do: transformers.js 라이브러리 중 pipeline 함수를 import하십시오.
// Make it available globally
window.pipeline = pipeline;
</script>
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/dist/css/bootstrap.min.css" rel="stylesheet">
<link rel="stylesheet" href="css/styles.css">
</head>
<body>
<div class="container-main">
<!-- Back to Home button -->
<div class="row mt-5">
<div class="col-md-12 text-center">
<a href="index.html" class="btn btn-outline-secondary"
style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a>
</div>
</div>
<!-- Content -->
<div class="container mt-5">
<!-- Centered Titles -->
<div class="text-center">
<h2>Natural Language Processing</h2>
<h4>Zero Shot Classification</h4>
</div>
<!-- Actual Content of this page -->
<div id="zero-shot-classification-container" class="container mt-4">
<h5>Zero Shot Classification with Xenova/mobilebert-uncased-mnli:</h5>
<div class="d-flex align-items-center mb-2">
<label for="textText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Text:</label>
<input type="text" class="form-control flex-grow-1" id="textText" value="Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app."
placeholder="Enter text" style="margin-right: 15px; margin-left: 15px;">
</div>
<div class="d-flex align-items-center">
<label for="labelsText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma separated):</label>
<input type="text" class="form-control flex-grow-1" id="labelsText" value="mobile, billing, website, account access"
placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;">
<button id="classifyButton" class="btn btn-primary ml-2"
onclick="classifyText()">Classify</button>
</div>
<div class="mt-4">
<h4>Output:</h4>
<pre id="outputArea"></pre>
</div>
</div>
<hr> <!-- Line Separator -->
<div id="zero-shot-classification-container-multi" class="container mt-4">
<h5>Zero Shot Classification with Xenova/nli-deberta-v3-xsmall (Multi-Label):</h5>
<div class="d-flex align-items-center mb-2">
<label for="textTextMulti" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Text:</label>
<input type="text" class="form-control flex-grow-1" id="textTextMulti" value="I have a problem with my iphone that needs to be resolved asap!"
placeholder="Enter text" style="margin-right: 15px; margin-left: 15px;">
</div>
<div class="d-flex align-items-center">
<label for="labelsTextMulti" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma separated):</label>
<input type="text" class="form-control flex-grow-1" id="labelsTextMulti" value="urgent, not urgent, phone, tablet, computer"
placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;">
<button id="classifyButtonMulti" class="btn btn-primary ml-2"
onclick="classifyTextMulti()">Classify</button>
</div>
<div class="mt-4">
<h4>Output:</h4>
<pre id="outputAreaMulti"></pre>
</div>
</div>
<!-- Back to Home button -->
<div class="row mt-5">
<div class="col-md-12 text-center">
<a href="index.html" class="btn btn-outline-secondary"
style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a>
</div>
</div>
</div>
</div>
<script>
let classifier;
let classifierMulti;
// Initialize the sentiment analysis model
async function initializeModel() {
// To-Do: pipeline 함수에 task와 model을 지정하여 zero 샷 분류 모델을 생성하여 classifier에 저장하십시오. 모델은 Xenova/mobilebert-uncased-mnli 사용
// To-Do: pipeline 함수에 task와 model을 지정하여 zero 샷 분류 모델을 생성하여 classifierMulti에 저장하십시오. 모델은 Xenova/nli-deberta-v3-xsmall 사용
}
async function classifyText() {
const text = document.getElementById("textText").value.trim();
const labels = document.getElementById("labelsText").value.trim().split(",").map(item => item.trim());
const result = await classifier(text, labels);
document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2);
}
async function classifyTextMulti() {
const text = document.getElementById("textTextMulti").value.trim();
const labels = document.getElementById("labelsTextMulti").value.trim().split(",").map(item => item.trim());
const result = await classifierMulti(text, labels, { multi_label: true });
document.getElementById("outputAreaMulti").innerText = JSON.stringify(result, null, 2);
}
// Initialize the model after the DOM is completely loaded
window.addEventListener("DOMContentLoaded", initializeModel);
</script>
</body>
</html> |