README.md

# Question Answering Dataset

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.

## Features

- **Context passages**: Rich contextual information for comprehension
- **Questions and answers**: Paired Q&A sets for training
- **Multiple domains**: Diverse topics and subject areas
- **SQuAD format**: Standard format compatible with popular QA frameworks
- **Ready for transformer models**: Optimized for BERT, GPT, and other transformer architectures

## Technologies

- JSON
- CSV
- Transformers
- BERT
- GPT

## Difficulty

**Advanced**

## Dataset Structure

The dataset is available in multiple formats:

### SQuAD Format (JSON)
- `squad_format.json`: Standard SQuAD 2.0 format
- Compatible with Hugging Face Transformers library

### CSV Format
- `dataset.csv`: Simple CSV format for easy data manipulation
- Columns: context, question, answer, domain

### Individual Files
- `contexts.json`: All context passages
- `questions.json`: All questions with context references
- `answers.json`: All answers with question references

## Installation

```bash
# Clone or download the dataset
git clone <repository-url>
cd question-answering
```

## Usage

### Using with Hugging Face Transformers

```python
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")
```

### Using with CSV

```python
import pandas as pd

# Load CSV dataset
df = pd.read_csv('dataset.csv')
print(df.head())
```

## Dataset Statistics

- **Total Context Passages**: 1000+
- **Total Questions**: 5000+
- **Total Answers**: 5000+
- **Domains**: Science, History, Literature, Technology, General Knowledge

## File Structure

```
question-answering/
├── README.md
├── index.html
├── styles.css
├── script.js
├── squad_format.json
├── dataset.csv
├── contexts.json
├── questions.json
├── answers.json
├── generate_question_papers.py
├── question-papers/
│ ├── index.html
│ ├── metadata.json
│ ├── Class-1/
│ │ ├── 2020/
│ │ │ ├── Class-1-Question-Paper-2020.pdf
│ │ │ └── Class-1-Answer-Key-2020.pdf
│ │ ├── 2021/
│ │ ├── ... (all years)
│ ├── Class-2/
│ ├── ... (Class 1-12)
├── examples/
│ ├── bert_example.py
│ ├── gpt_example.py
│ └── transformers_example.py
└── LICENSE
```

## Question Papers

This project includes question papers and answer keys for all classes (Class 1 to Class 12) from previous years (2020-2025).

### Features:
- **12 Classes**: Class 1 through Class 12
- **6 Years**: 2020, 2021, 2022, 2023, 2024, 2025
- **PDF Format**: Professional question papers and answer keys
- **Publicly Available**: Free to download and use
- **Total Files**: 144 PDFs (72 question papers + 72 answer keys)

### Access Question Papers:
- **Browse Online**: Open `question-papers/index.html` in your browser
- **Direct Download**: Navigate to `question-papers/[Class]/[Year]/` directory
- **Generate More**: Run `python generate_question_papers.py` to create additional papers

### Subjects Covered:
- **Primary Classes (1-5)**: English, Mathematics, Science, General Knowledge, Social Studies
- **Middle Classes (6-8)**: English, Mathematics, Science, Social Studies
- **Secondary Classes (9-10)**: English, Mathematics, Science, Social Studies
- **Senior Classes (11-12)**: English, Mathematics, Physics, Chemistry, Biology

## Examples

See the `examples/` directory for complete working examples:
- BERT-based QA model training
- GPT-based QA inference
- Transformers library integration

## Citation

If you use this dataset in your research, please cite:

```
@dataset{question_answering_2025,
title={Question Answering Dataset},
author={Molla Samser},
year={2025},
url={https://rskworld.in},
publisher={RSK World}
}
```

## License

See LICENSE file for details.

## Contact

For questions, suggestions, or contributions:

- **Author**: Molla Samser
- **Website**: [https://rskworld.in](https://rskworld.in)
- **Email**: help@rskworld.in
- **Phone**: +91 93305 39277

## Acknowledgments

This dataset is maintained by RSK World. For more datasets and projects, visit [rskworld.in](https://rskworld.in).

index.html

Raw Download

<!--
    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>
    <link rel="stylesheet" href="styles.css">
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">
</head>
<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>
    </main>

    <footer>
        <div class="container">
            <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>
        </div>
    </footer>

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