RSK World - Text Classification Dataset | NLP | Multi-Class Classification | Machine Learning | RSK World

Text Classification NLP Machine Learning 6 Classes Python BERT Ready

This project features a comprehensive Text Classification dataset designed for professional NLP, document categorization, and machine learning applications. The dataset includes 300+ labeled documents across 6 categories: Technology, Sports, Politics, Entertainment, Business, and Science. Features train/validation/test splits and multiple formats (CSV, JSON, TXT). Includes powerful Python scripts: preprocessing.py for text cleaning, train_classifier.py for traditional ML models, train_transformers.py for BERT/RoBERTa training, data_augmentation.py with 6 techniques, api_server.py for REST API deployment, model_explainability.py for LIME-based explanations, batch_processor.py for high-throughput classification, and visualizations.py for word clouds and charts. The package includes interactive data explorer, live classifier demo, analytics dashboard, comprehensive README.md, and MIT License. Perfect for data scientists, researchers, students, and developers working on news categorization, topic classification, document analysis, and NLP education projects.

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Dataset Overview

Complete text classification dataset with 300+ labeled documents across 6 categories for NLP and machine learning.

300+ labeled documents
6 category classes
Technology, Sports, Politics
Entertainment, Business, Science
News articles included
Topic classification ready
Pre-split: Train (240), Val (30), Test (30)
Average text length: ~20 words
Balanced class distribution
Clean, high-quality labels
Perfect for NLP & ML training

Dataset Structure & Files

Well-organized folder structure with training, validation, and test splits plus comprehensive data files.

data/csv/train.csv - Training set (240)
data/csv/validation.csv - Validation set
data/csv/test.csv - Test set
data/csv/full_dataset.csv - Complete data
data/json/dataset.json - JSON format
data/json/full_dataset.json - Full JSON
data/txt/categories.txt - Category labels
scripts/ - Python utilities
Consistent naming convention
Easy to load with pandas
Transformer-ready format

Machine Learning Training

Complete training pipeline with support for traditional ML and transformer-based models.

Naive Bayes classifier (85.2%)
Logistic Regression (89.7%)
Linear SVM (88.9%)
BERT fine-tuning (94.3%)
TF-IDF vectorization
Count vectorization
Cross-validation support
Model checkpointing
Performance metrics report
Hyperparameter tuning
Model export & persistence

Multiple File Formats

Dataset available in multiple formats for maximum compatibility with different NLP tools and frameworks.

CSV format (.csv files)
JSON format with metadata
Plain text format (.txt)
Pandas DataFrame ready
HuggingFace compatible
Transformer pipeline ready
Easy format conversion
Unicode text support
UTF-8 encoding
Comment lines for metadata
Header row included

Analysis & Visualization

Comprehensive analysis tools with visualization capabilities and interactive explorer.

Interactive Data Explorer
Category distribution charts
Word frequency analysis
Word cloud generation
Text length histogram
Confusion matrix plots
Performance benchmarking
Model comparison tools
HTML report generation
Export visualization images
Analytics Dashboard

Compatible Frameworks

Works with all major NLP frameworks and libraries out of the box.

HuggingFace Transformers
BERT / RoBERTa / DistilBERT
scikit-learn ML library
pandas data manipulation
Flask REST API
LIME explainability
matplotlib visualization
wordcloud generation
TensorFlow/Keras ready
PyTorch compatible
Jupyter Notebook support

What You Get

Complete package with all files needed for professional text classification projects.

300+ labeled documents
10+ Python utility scripts
preprocessing.py - Text preprocessor
train_classifier.py - Traditional ML
train_transformers.py - BERT training
data_augmentation.py - 6 techniques
api_server.py - REST API server
model_explainability.py - LIME
batch_processor.py - Batch processing
visualizations.py - Charts & clouds
Interactive demo website

Interactive Demo Website

Beautiful demo website with data explorer, live classifier, analytics dashboard, and comprehensive guide.

Modern animated design
Interactive Data Explorer
Live Text Classifier
Analytics Dashboard
Filter by category
Real-time predictions
Category visualization
Performance metrics display
Step-by-step usage guide
Dark theme with gradients
Fully responsive layout

Python Scripts Included

Professional Python scripts for preprocessing, training, augmentation, API deployment, and explainability.

preprocessing.py - Text cleaning & tokenization
train_classifier.py - Traditional ML training
train_transformers.py - BERT/Transformer training
data_augmentation.py - 6 augmentation techniques
api_server.py - Flask REST API server
model_explainability.py - LIME explanations
batch_processor.py - High-throughput processing
visualizations.py - Charts & word clouds
hyperparameter_tuning.py - Optimization
data_quality.py - Quality checks
deep_learning.py - Neural networks

Classification Categories

6 distinct categories covering major news and document classification domains.

Technology - Tech news, gadgets, AI (🔵 Blue)
Sports - Athletics, competitions, leagues (🟢 Green)
Politics - Government, policy, elections (🟣 Purple)
Entertainment - Movies, music, TV (🩷 Pink)
Business - Finance, markets, economy (🟡 Amber)
Science - Research, discoveries, health (🔵 Cyan)
Clear labeling criteria
Human-verified labels
Balanced distribution
Easy to extend classes
Total: 300+ samples

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
HuggingFace Transformers - BERT, RoBERTa (Apache 2.0)
scikit-learn - Machine Learning (BSD License)
Flask - REST API Framework (BSD License)
LIME - Model Explainability (BSD License)
matplotlib - Data Visualization (PSF License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
MIT License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Text Classification Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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Text Classification Dataset

Dataset Overview

Dataset Structure & Files

Machine Learning Training

Multiple File Formats

Analysis & Visualization

Compatible Frameworks

What You Get

Interactive Demo Website

Python Scripts Included

Classification Categories

Credits & Acknowledgments

Support & Contact

Featured Content

Additional Sponsored Content

Download Free Source Code

Quick Links

Categories

Technologies

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