File size: 6,103 Bytes
fea0eb7 fabf26d fea0eb7 d0c56e2 fea0eb7 fabf26d fea0eb7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
<!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/[email protected]/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> |