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    <title>Question Answering - Hugging Face Transformers.js</title>

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        // Import the library
        import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4';

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            <div class="header-main-text">
                <h1>Hugging Face Transformers.js</h1>
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            <div class="header-sub-text">
                <h3>Free AI Models for JavaScript Web Development</h3>
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        <!-- Content -->
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                <h2>Natural Language Processing</h2>
                <h4>Question Answering</h4>
            </div>

            <!-- Actual Content of this page -->
            <div id="question-answering-container" class="container mt-4">
                <h5>Question Answering:</h5>
                <div class="d-flex align-items-center mb-2">
                    <label for="questionText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Question:</label>
                    <input type="text" class="form-control flex-grow-1" id="questionText" value="Who was Jim Henson?"
                        placeholder="Enter question text" style="margin-right: 15px; margin-left: 15px;">
                </div>
                <div class="d-flex align-items-center">
                    <label for="contextText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Context:</label>
                    <input type="text" class="form-control flex-grow-1" id="contextText" value="Jim Henson was a nice puppet."
                        placeholder="Enter context text" style="margin-right: 15px; margin-left: 15px;">
                    <button id="answerButton" class="btn btn-primary ml-2"
                        onclick="answerQuestion()">Answer</button>
                </div>
                <div class="mt-4">
                    <h4>Output:</h4>
                    <pre id="outputArea"></pre>
                </div>
            </div>

            <hr> <!-- Line Separator -->

            <div id="question-answering-container2" class="container mt-4">
                <h5>Question Answering (Top Results):</h5>
                <div class="d-flex align-items-center mb-2">
                    <label for="questionTopText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Question:</label>
                    <input type="text" class="form-control flex-grow-1" id="questionTopText" value="What is the temperature today?"
                        placeholder="Enter question text" style="margin-right: 15px; margin-left: 15px;">
                </div>
                <div class="d-flex align-items-center">
                    <label for="contextTopText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Context:</label>
                    <input type="text" class="form-control flex-grow-1" id="contextTopText" value="It is very hot today."
                        placeholder="Enter context text" style="margin-right: 15px; margin-left: 15px;">
                    <button id="answerTopButton" class="btn btn-primary ml-2"
                        onclick="answerQuestionTop()">Answer</button>
                </div>
                <div class="mt-4">
                    <h4>Output:</h4>
                    <pre id="outputTopArea"></pre>
                </div>
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    <script>

        let answerer;

        // Initialize the sentiment analysis model
        async function initializeModel() {
            answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad');

        }

        async function answerQuestion() {
            const question = document.getElementById("questionText").value.trim();
            const context = document.getElementById("contextText").value.trim();

            const result = await answerer(question, context);

            document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2);
        }

        async function answerQuestionTop() {
            const question = document.getElementById("questionTopText").value.trim();
            const context = document.getElementById("contextTopText").value.trim();

            const result = await answerer(question, context, { topk: 3 });

            document.getElementById("outputTopArea").innerText = JSON.stringify(result, null, 2);
        }

        // Initialize the model after the DOM is completely loaded
        window.addEventListener("DOMContentLoaded", initializeModel);
    </script>
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