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RSK World
question-answering
RSK World
question-answering
Question Answering Dataset - Question Answering + Reading Comprehension + SQuAD Format + NLP
question-answering
  • examples
  • question-papers
  • .gitignore535 B
  • BOARDS_INDEX_FIXES.md3.3 KB
  • BOARD_PAPERS_SUMMARY.md6.3 KB
  • DOWNLOAD_REAL_PAPERS_INSTRUCTIONS.md3.2 KB
  • INDEX_FIXES_SUMMARY.md3.3 KB
  • LICENSE1.4 KB
  • METADATA.json1.5 KB
  • PDF_DOWNLOAD_FIXES.md4.1 KB
  • PROJECT_SUMMARY.md4.6 KB
  • QUESTION_COLLECTION_GUIDE.md2.2 KB
  • QUESTION_PAPERS_SUMMARY.md4.6 KB
  • README.md5 KB
  • README_REAL_PAPERS.md4.2 KB
  • SUMMARY_REAL_PAPERS.md4.1 KB
  • answers.json1.9 KB
  • contexts.json2.4 KB
  • dataset.csv4.5 KB
  • dataset_metadata.txt878 B
  • download_real_question_papers.py13.3 KB
  • fetch_real_question_papers.py11 KB
  • generate_board_question_papers.py22.8 KB
  • generate_question_papers.py13.9 KB
  • index.html8.4 KB
  • questions.json1.8 KB
  • real_question_data.json1.8 KB
  • requirements.txt417 B
  • script.js2.9 KB
  • squad_format.json5.9 KB
  • styles.css5.7 KB
index.html
index.html
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<!--
    Project: Question Answering Dataset
    Description: A dataset containing context passages, questions, and answers for training QA models and reading comprehension systems.
    Author: Molla Samser
    Website: https://rskworld.in
    Contact: help@rskworld.in
    Phone: +91 93305 39277
-->
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <meta name="description" content="Question Answering Dataset - A comprehensive dataset for training QA models and reading comprehension systems">
    <meta name="author" content="Molla Samser">
    <title>Question Answering Dataset - RSK World</title>
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<body>
    <header>
        <div class="container">
            <h1><i class="fas fa-file-alt text-danger"></i> Question Answering Dataset</h1>
            <p class="subtitle">A comprehensive dataset for training QA models and reading comprehension systems</p>
        </div>
    </header>

    <main class="container">
        <section class="intro">
            <h2>About This Dataset</h2>
            <p>This dataset contains context passages with corresponding questions and answers for question answering tasks. Perfect for training QA models, reading comprehension systems, and transformer-based language models.</p>
        </section>

        <section class="features">
            <h2><i class="fas fa-star"></i> Features</h2>
            <div class="features-grid">
                <div class="feature-card">
                    <i class="fas fa-book"></i>
                    <h3>Context Passages</h3>
                    <p>Rich contextual information for comprehension</p>
                </div>
                <div class="feature-card">
                    <i class="fas fa-question-circle"></i>
                    <h3>Questions and Answers</h3>
                    <p>Paired Q&A sets for training</p>
                </div>
                <div class="feature-card">
                    <i class="fas fa-globe"></i>
                    <h3>Multiple Domains</h3>
                    <p>Diverse topics and subject areas</p>
                </div>
                <div class="feature-card">
                    <i class="fas fa-code"></i>
                    <h3>SQuAD Format</h3>
                    <p>Standard format compatible with popular QA frameworks</p>
                </div>
                <div class="feature-card">
                    <i class="fas fa-robot"></i>
                    <h3>Ready for Transformers</h3>
                    <p>Optimized for BERT, GPT, and other transformer architectures</p>
                </div>
            </div>
        </section>

        <section class="technologies">
            <h2><i class="fas fa-tools"></i> Technologies</h2>
            <div class="tech-tags">
                <span class="tech-tag">JSON</span>
                <span class="tech-tag">CSV</span>
                <span class="tech-tag">Transformers</span>
                <span class="tech-tag">BERT</span>
                <span class="tech-tag">GPT</span>
            </div>
        </section>

        <section class="difficulty">
            <h2><i class="fas fa-chart-line"></i> Difficulty Level</h2>
            <p class="difficulty-badge advanced">Advanced</p>
        </section>

        <section class="dataset-preview">
            <h2><i class="fas fa-database"></i> Dataset Preview</h2>
            <div id="dataset-preview" class="preview-container">
                <p>Loading dataset preview...</p>
            </div>
        </section>

        <section class="download">
            <h2><i class="fas fa-download"></i> Download</h2>
            <div class="download-buttons">
                <a href="squad_format.json" class="btn btn-primary" download>
                    <i class="fas fa-file-code"></i> SQuAD Format (JSON)
                </a>
                <a href="dataset.csv" class="btn btn-secondary" download>
                    <i class="fas fa-file-csv"></i> CSV Format
                </a>
                <a href="contexts.json" class="btn btn-secondary" download>
                    <i class="fas fa-file-alt"></i> Contexts (JSON)
                </a>
                <a href="questions.json" class="btn btn-secondary" download>
                    <i class="fas fa-question-circle"></i> Questions (JSON)
                </a>
                <a href="answers.json" class="btn btn-secondary" download>
                    <i class="fas fa-check-circle"></i> Answers (JSON)
                </a>
            </div>
        </section>

        <section class="question-papers">
            <h2><i class="fas fa-file-pdf"></i> Question Papers</h2>
            <p>Access question papers and answer keys for all classes, boards, and competitive exams from previous years (2020-2025). All papers are publicly available and free to download.</p>
            <div class="download-buttons" style="margin-top: 1.5rem;">
                <a href="question-papers/index.html" class="btn btn-primary">
                    <i class="fas fa-book"></i> Browse All Question Papers
                </a>
                <a href="question-papers/boards/index.html" class="btn btn-primary">
                    <i class="fas fa-graduation-cap"></i> Board & Competitive Exams
                </a>
            </div>
            <div class="info-box" style="margin-top: 1.5rem; padding: 1rem; background: #f8f9fa; border-radius: 5px;">
                <p><strong>Question Papers Available:</strong></p>
                <ul style="margin-left: 2rem; margin-top: 0.5rem;">
                    <li><strong>General Papers:</strong> Class 1 to Class 12 (Structure ready for real papers)</li>
                    <li><strong>West Bengal Board:</strong> WBBSE (Class 10) & WBCHSE (Class 12)</li>
                    <li><strong>CBSE Board:</strong> Class 10 & Class 12</li>
                    <li><strong>Competitive Exams:</strong> JEE Main, JEE Advanced, NIT, WBJEE</li>
                    <li><strong>Years:</strong> 2020, 2021, 2022, 2023, 2024, 2025</li>
                    <li><strong>Note:</strong> Real question papers need to be downloaded from official sources. See <a href="README_REAL_PAPERS.md" target="_blank" style="color: #667eea;">README_REAL_PAPERS.md</a> for instructions.</li>
                </ul>
            </div>
        </section>

        <section class="examples">
            <h2><i class="fas fa-code"></i> Code Examples</h2>
            <div class="code-examples">
                <div class="code-block">
                    <h3>Using with Hugging Face Transformers</h3>
                    <pre><code>from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import json

# Load the dataset
with open('squad_format.json', 'r') as f:
    dataset = json.load(f)

# Load a pre-trained model
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased")</code></pre>
                </div>
                <div class="code-block">
                    <h3>Using with CSV</h3>
                    <pre><code>import pandas as pd

# Load CSV dataset
df = pd.read_csv('dataset.csv')
print(df.head())</code></pre>
                </div>
            </div>
        </section>

        <section class="contact">
            <h2><i class="fas fa-envelope"></i> Contact</h2>
            <div class="contact-info">
                <p><strong>Author:</strong> Molla Samser</p>
                <p><strong>Website:</strong> <a href="https://rskworld.in" target="_blank">https://rskworld.in</a></p>
                <p><strong>Email:</strong> <a href="mailto:help@rskworld.in">help@rskworld.in</a></p>
                <p><strong>Phone:</strong> <a href="tel:+919330539277">+91 93305 39277</a></p>
            </div>
        </section>
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            <p>&copy; 2025 <a href="https://rskworld.in" target="_blank">RSK World</a>. All rights reserved.</p>
            <p>Created by <a href="https://rskworld.in" target="_blank">Molla Samser</a></p>
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About RSK World

Founded by Molla Samser, with Designer & Tester Rima Khatun, RSK World is your one-stop destination for free programming resources, source code, and development tools.

Founder: Molla Samser
Designer & Tester: Rima Khatun

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