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<title>Token Classification - Hugging Face Transformers.js</title>
<script type="module">
// Import the library
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4';
// Make it available globally
window.pipeline = pipeline;
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<div class="header">
<div class="header-logo">
<img src="images/logo.png" alt="logo">
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<div class="header-main-text">
<h1>Hugging Face Transformers.js</h1>
</div>
<div class="header-sub-text">
<h3>Free AI Models for JavaScript Web Development</h3>
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<h2>Natural Language Processing</h2>
<h4>Token Classification (Named Entity Recognition)</h4>
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<div id="token-classification-container" class="container mt-4">
<h5>Perform Named Entity Recognition with Xenova/bert-base-NER:</h5>
<div class="d-flex align-items-center">
<label for="tokenClassificationText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Text to Recognize:</label>
<input type="text" class="form-control flex-grow-1" id="tokenClassificationText"
value="My name is Sarah and I live in London" placeholder="Enter text"
style="margin-right: 15px; margin-left: 15px;">
<button id="classifyButton" class="btn btn-primary" onclick="analyzeText()">analyze</button>
</div>
<div class="mt-4">
<h4>Output:</h4>
<pre id="outputArea"></pre>
</div>
</div>
<hr> <!-- Line Separator -->
<div id="token-classification-container2" class="container mt-4">
<h5>Perform Named Entity Recognition with Xenova/bert-base-NER (Return all Labels):</h5>
<div class="d-flex align-items-center">
<label for="tokenClassificationText2" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Text to Recognize:</label>
<input type="text" class="form-control flex-grow-1" id="tokenClassificationText2"
value="Sarah lives in the United States of America" placeholder="Enter text"
style="margin-right: 15px; margin-left: 15px;">
<button id="classifyButton2" class="btn btn-primary" onclick="analyzeText2()">analyze</button>
</div>
<div class="mt-4">
<h4>Output:</h4>
<pre id="outputArea2"></pre>
</div>
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<script>
let classifier;
// Initialize the sentiment analysis model
async function initializeModel() {
classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
}
async function analyzeText() {
const textFieldValue = document.getElementById("tokenClassificationText").value.trim();
const result = await classifier(textFieldValue);
document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2);
}
async function analyzeText2() {
const textFieldValue = document.getElementById("tokenClassificationText2").value.trim();
const result = await classifier(textFieldValue, { ignore_labels: [] });
document.getElementById("outputArea2").innerText = JSON.stringify(result, null, 2);
}
// Initialize the model after the DOM is completely loaded
window.addEventListener("DOMContentLoaded", initializeModel);
</script>
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