RSK World - Text Classification Dataset - Project Files Browser | RSK World

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================================================================================
  Text Classification Dataset Project
================================================================================
  Project: Text Classification Dataset
  Category: Text Data / NLP
  Description: Multi-class text classification dataset with labeled documents 
               for news categorization, topic classification, and document analysis.
  
  Author: Molla Samser
  Designer & Tester: Rima Khatun
  Website: https://rskworld.in
  Email: help@rskworld.in | support@rskworld.in
  Phone: +91 93305 39277
  
  Copyright (c) 2026 RSK World - All Rights Reserved
  This content is provided for educational purposes only.
  
  Created: December 2026
  Last Modified: December 2026
================================================================================
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                    Text Classification
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                    Multi-class text classification dataset with labeled documents for news categorization, 
                    topic classification, and document analysis. Perfect for NLP model training and research.
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                        <span class="window-title">text_classifier.py</span>
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                    <pre class="code-content"><code><span class="comment"># Text Classification with Transformers</span>
<span class="keyword">from</span> transformers <span class="keyword">import</span> AutoTokenizer
<span class="keyword">from</span> transformers <span class="keyword">import</span> AutoModelForSequenceClassification

<span class="comment"># Load pretrained model</span>
model_name = <span class="string">"bert-base-uncased"</span>
tokenizer = AutoTokenizer.from_pretrained(model_name)

<span class="comment"># Categories</span>
categories = [
    <span class="string">"Technology"</span>, <span class="string">"Sports"</span>,
    <span class="string">"Politics"</span>, <span class="string">"Entertainment"</span>,
    <span class="string">"Business"</span>, <span class="string">"Science"</span>
]

<span class="comment"># Classify text</span>
<span class="keyword">def</span> <span class="function">classify</span>(text):
    inputs = tokenizer(text, return_tensors=<span class="string">"pt"</span>)
    outputs = model(**inputs)
    <span class="keyword">return</span> categories[outputs.argmax()]</code></pre>
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                <span class="section-badge">About Dataset</span>
                <h2 class="section-title">Comprehensive Text Classification Resource</h2>
                <p class="section-subtitle">
                    This dataset includes labeled documents across multiple categories for text classification tasks. 
                    Perfect for news categorization, topic classification, document analysis, and NLP model training.
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            <div class="overview-grid">
                <div class="overview-card">
                    <div class="card-icon">
                        <i class="fas fa-newspaper"></i>
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                    <h3>News Categorization</h3>
                    <p>Classify news articles into categories like technology, sports, politics, and more.</p>
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                    <h3>Topic Classification</h3>
                    <p>Identify main topics and themes from unstructured text documents.</p>
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                    <h3>Document Analysis</h3>
                    <p>Analyze and categorize large volumes of documents automatically.</p>
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                    <h3>Model Training</h3>
                    <p>Train and fine-tune transformer models like BERT for text classification.</p>
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                        <h3>Multiple Document Categories</h3>
                        <p>6 distinct categories covering technology, sports, politics, entertainment, business, and science topics.</p>
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                        <h3>Labeled Training Data</h3>
                        <p>All documents are professionally labeled with accurate category assignments for supervised learning.</p>
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                        <h3>Preprocessed Versions</h3>
                        <p>Includes cleaned, tokenized, and normalized versions ready for immediate use.</p>
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                        <h3>Transformer Ready Format</h3>
                        <p>Formatted for direct use with BERT, RoBERTa, and other transformer architectures.</p>
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                                    <td>1</td>
                                    <td>Apple announces new iPhone with revolutionary AI features...</td>
                                    <td><span class="category-badge tech">Technology</span></td>
                                    <td>0</td>
                                </tr>
                                <tr>
                                    <td>2</td>
                                    <td>Manchester United wins Premier League title after dramatic finish...</td>
                                    <td><span class="category-badge sports">Sports</span></td>
                                    <td>1</td>
                                </tr>
                                <tr>
                                    <td>3</td>
                                    <td>Senate passes new climate bill with bipartisan support...</td>
                                    <td><span class="category-badge politics">Politics</span></td>
                                    <td>2</td>
                                </tr>
                                <tr>
                                    <td>4</td>
                                    <td>Marvel releases trailer for upcoming superhero movie...</td>
                                    <td><span class="category-badge entertainment">Entertainment</span></td>
                                    <td>3</td>
                                </tr>
                                <tr>
                                    <td>5</td>
                                    <td>Stock market reaches all-time high amid economic recovery...</td>
                                    <td><span class="category-badge business">Business</span></td>
                                    <td>4</td>
                                </tr>
                                <tr>
                                    <td>6</td>
                                    <td>NASA discovers new exoplanet potentially habitable...</td>
                                    <td><span class="category-badge science">Science</span></td>
                                    <td>5</td>
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                        <pre class="code-content json-preview"><code>{
  <span class="json-key">"dataset"</span>: {
    <span class="json-key">"name"</span>: <span class="json-string">"Text Classification Dataset"</span>,
    <span class="json-key">"version"</span>: <span class="json-string">"1.0.0"</span>,
    <span class="json-key">"total_samples"</span>: <span class="json-number">10000</span>,
    <span class="json-key">"categories"</span>: [
      <span class="json-string">"Technology"</span>,
      <span class="json-string">"Sports"</span>,
      <span class="json-string">"Politics"</span>,
      <span class="json-string">"Entertainment"</span>,
      <span class="json-string">"Business"</span>,
      <span class="json-string">"Science"</span>
    ]
  },
  <span class="json-key">"samples"</span>: [
    {
      <span class="json-key">"id"</span>: <span class="json-number">1</span>,
      <span class="json-key">"text"</span>: <span class="json-string">"Apple announces new iPhone..."</span>,
      <span class="json-key">"category"</span>: <span class="json-string">"Technology"</span>,
      <span class="json-key">"label"</span>: <span class="json-number">0</span>
    }
  ]
}</code></pre>
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                            <h3>Dataset Statistics</h3>
                            <ul class="stats-list">
                                <li><span>Total Documents:</span> <strong>10,000</strong></li>
                                <li><span>Training Set:</span> <strong>7,000 (70%)</strong></li>
                                <li><span>Validation Set:</span> <strong>1,500 (15%)</strong></li>
                                <li><span>Test Set:</span> <strong>1,500 (15%)</strong></li>
                                <li><span>Avg. Document Length:</span> <strong>256 tokens</strong></li>
                                <li><span>Vocabulary Size:</span> <strong>50,000+</strong></li>
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                    <div class="step-number">01</div>
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                        <h3>Download Dataset</h3>
                        <p>Download the dataset in your preferred format (CSV, JSON, or TXT).</p>
                        <div class="code-snippet">
                            <code>wget https://rskworld.in/datasets/text-classification.zip</code>
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                        <h3>Load Data</h3>
                        <p>Load the dataset using pandas or your preferred library.</p>
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                            <code>import pandas as pd<br>df = pd.read_csv('train.csv')</code>
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                        <h3>Preprocess</h3>
                        <p>Apply tokenization and preprocessing using provided scripts.</p>
                        <div class="code-snippet">
                            <code>from preprocessing import TextPreprocessor<br>preprocessor = TextPreprocessor()</code>
                        </div>
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                    <div class="step-number">04</div>
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                        <h3>Train Model</h3>
                        <p>Train your classification model using transformers or sklearn.</p>
                        <div class="code-snippet">
                            <code>model.fit(X_train, y_train)<br>predictions = model.predict(X_test)</code>
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