RSK World - Named Entity Recognition Dataset | NER | Information Extraction | BIO Format | NLP | RSK World

Named Entity Recognition Dataset - RSK World

Named Entity Recognition Information Extraction BIO Format spaCy & NLTK Python Transformers

This project features a comprehensive Named Entity Recognition Dataset designed for professional NER systems, information extraction, NLP, and machine learning applications. The dataset includes labeled entities including persons, organizations, locations, dates, and other entity types in BIO format. Includes powerful Python scripts: examples for NER models, spaCy, NLTK, Transformers, entity visualization, interactive demo, and complete documentation. Also includes interactive demo website. The package includes interactive demo website, comprehensive README.md, and MIT License. Perfect for NLP researchers, data scientists, students, and developers working on named entity recognition, information extraction, NLP, and machine learning projects.

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Dataset Overview

Complete Named Entity Recognition Dataset with labeled entities including persons, organizations, locations, dates, and other entity types in BIO format for NER systems, information extraction, and NLP applications.

Labeled entities - Comprehensive labeled entities for NER tasks
Multiple entity types - PERSON, ORG, LOC, DATE, MONEY, PERCENT
BIO format - Standard BIO tagging format for sequence labeling
BIO format - Standard BIO format for compatibility
NER ready - Preprocessed data ready for NER models
Information extraction - Perfect for information extraction tasks
Multiple formats - JSON, CSV, BIO formats supported
Multiple Python scripts included for spaCy, NLTK, Transformers
Perfect for named entity recognition, information extraction, NLP & machine learning applications

Dataset Structure & Files

Well-organized project structure with labeled entities, Python scripts for NER models, spaCy, NLTK, Transformers, and interactive demo.

train.json - Training dataset with labeled entities
test.json - Test dataset with labeled entities
train.csv - CSV format training dataset
test.csv - CSV format test dataset
train_bio.txt - BIO format training dataset
scripts/load_dataset.py - Dataset loading script
scripts/train_model.py - NER model training script
scripts/visualize_ner.py - Entity visualization script
scripts/batch_process.py - Batch processing script
scripts/api_server.py - Flask API server
scripts/advanced_stats.py - Statistics and analytics
scripts/export_data.py - Multi-format export
scripts/evaluate_model.py - Model evaluation
index.html - Interactive demo website
demo.html - Interactive NER demo
README.md - Comprehensive project documentation
requirements.txt - Python dependencies (spacy, nltk, transformers)
LICENSE - MIT License file
.gitignore - Git ignore configuration
Consistent directory structure with train/test split
Easy to load with load_dataset.py script
Organized structure (dataset, scripts)
Label-based organization by entity type
Visualization with entity highlighting support
Complete preprocessing pipeline ready

Named Entity Recognition & Processing

Complete NER pipeline with support for spaCy, NLTK, Transformers, information extraction, and advanced NLP features.

spaCy Models - Use spaCy for named entity recognition tasks
NLTK Models - Use NLTK for entity extraction and tagging
Transformers - Leverage Hugging Face Transformers library
Information Extraction - Extract entities from text documents
BIO Format - Standard format for NER datasets
Text Processing - Process and tokenize text documents
Entity Tagging - Tag entities with BIO labels
Entity Extraction - Extract entities from text
Batch Processing - Process multiple documents efficiently
Model Training - Train NER models from dataset
Model Evaluation - Evaluate model performance on test set
Error Handling - Comprehensive error checking and informative messages
ML Ready - Preprocessed data for machine learning
Visualization Tools - Display entities and labels
Multiple Models - Support for spaCy, NLTK, and other transformer models
Data Export - Export entities and labels
Performance Optimized - Efficient batch operations and memory management

Data Formats & Compatibility

Dataset available in standard formats (JSON, CSV, BIO) for maximum compatibility with NLP libraries and ML frameworks.

JSON format - Standard JSON format for entities and labels
CSV format - CSV format for easy data manipulation
BIO format - Standard BIO tagging format for compatibility
NumPy array compatible - Easy conversion to numpy arrays for ML
Pandas ready - Direct loading with pandas DataFrame
Transformers compatible - Ready for Hugging Face Transformers
TensorFlow/PyTorch ready - Can be converted for deep learning models
Standard data formats - Widely supported JSON, CSV, and BIO formats
Easy to import and process - Simple data loading functions
Compatible with all ML libraries - Universal format support
Jupyter Notebook ready - Perfect for interactive NLP analysis
Python NLP processing ready - Native spacy, nltk, transformers support
spaCy/NLTK ready - Compatible with spaCy, NLTK, and other NLP libraries
NLP tools ready - Compatible with transformers, spacy, nltk
API integration ready - JSON format for NER results
Data validation support - Easy to validate data quality and format
NER ready - Compatible with spaCy and NLTK models
Information extraction ready - Real-time entity extraction from text

Analysis & Visualization

Comprehensive NER visualization tools with interactive viewer and analysis capabilities.

Interactive Entity Viewer - Entity display with highlighting
Multiple Entity Display - View entities with different types
Entity gallery - Browse through entities by type
Entity highlighting - Display entities highlighted in text
Entity comparison - Compare multiple entities side-by-side
Entity results visualization - Display entity results with confidence scores
Text visualization - Show text documents and entity spans
Entity-based filtering - Filter entities by type
Entity metadata display - Show entity type and position information
Entity quality highlighting - Highlight entity quality metrics
Dataset statistics - Comprehensive summary of NER dataset
Interactive entity viewer - Browse, search, and navigate entities
Entity distribution charts - Visualize entity type frequencies
Entity quality assessment - Display entity quality metrics
NER accuracy distribution - Show accuracy metrics
Entity preview grid - Grid view of entities by type
Export functionality - Download entities and labels
Responsive design - Works on desktop, tablet, and mobile devices

Compatible Frameworks

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

Scikit-learn ML library - Classification, clustering, preprocessing
Transformer Models - BERT, RoBERTa, and other transformer models
Deep Learning - TensorFlow, PyTorch, Keras compatibility
Hugging Face Transformers - Transformers library support
NLP Processing - spaCy, NLTK for text processing
NumPy numerical computing - Array operations for embeddings
Text processing - Tokenization, encoding, and preprocessing
matplotlib visualization - Static visualization and plots
Natural Language Processing - Text analysis and processing
spaCy library - NLP and text processing support
Flask REST API - Web API server for NER services
NER frameworks - Compatible with spaCy, NLTK, Transformers
Jupyter Notebook support - Interactive NLP analysis
Google Colab ready - Works in cloud-based notebooks
VS Code integration - Python extension support
PyCharm compatible - Full IDE support
NER models - Custom models for named entity recognition
NLP tools - Information extraction and entity tagging support
Transfer learning ready - Pre-trained transformer models
Real-time processing - Real-time entity extraction support
REST APIs - HTTP API for NER services

What You Get

Complete package with all files needed for professional NER systems, information extraction, NLP, and machine learning projects.

Labeled entities - Entities with types (PERSON, ORG, LOC, DATE, MONEY, PERCENT)
Training data - Training dataset with labeled entities
Test data - Test dataset with labeled entities
Python NER scripts - Complete NER system
scripts/train_model.py - NER model training with support for BIO format
scripts/visualize_ner.py - Entity visualization script
scripts/batch_process.py - Batch processing script
scripts/api_server.py - Flask API server
Organized directory structure - Separate folders for dataset, scripts
index.html - Interactive demo website
demo.html - Interactive NER demo
Multiple data formats - JSON, CSV, BIO formats supported
Complete documentation - README.md, PROJECT_STATUS.md, QUICKSTART.md
Documentation files - Comprehensive guides and project information
requirements.txt - All Python dependencies listed and versioned (spacy, nltk, transformers)
LICENSE - MIT License (free for commercial and non-commercial use)
Ready-to-use code examples - Copy and run scripts immediately
Data-based organization - Separate files for train, test datasets
BIO format organization - Data organized in BIO format
NER pipeline - Ready-to-use NER functions
Visualization tools - Interactive entity viewer
ML ready - Preprocessed data for model training

Interactive Demo Website

Beautiful demo website with NER explorer, entity gallery, and comprehensive guide.

Modern animated design - Smooth transitions and visual effects
Interactive NER Explorer - Browse and view entities
Entity Gallery - Display entities with text and labels
Entity Viewer - Browse, search, and navigate entities
Entity Metrics - Visual representation of entity results
Filter by type - Filter entities by entity type
Entity visualization - Display entities with text and entity highlights
Entity distribution - Entity type-based breakdown
Dataset statistics display - Total entities, types, accuracy
Interactive entity display - Click to view full entity details
Step-by-step usage guide - Comprehensive instructions
Dark theme with gradients - Modern, professional appearance
Fully responsive layout - Mobile, tablet, and desktop support
Data export options - Download entities and labels
Python scripts download - Access to all NER scripts
Interactive filters - Filter by entity type, confidence
Entity detail view - Individual entity display with metadata
Statistics summary - Quick overview of dataset metrics
No backend required - Pure HTML, CSS, JavaScript
Cross-browser compatible - Works on Chrome, Firefox, Safari, Edge

Python Scripts Included

Professional Python scripts for NER, information extraction, preprocessing, visualization, and advanced NLP features.

scripts/train_model.py - Comprehensive NER model training script
scripts/visualize_ner.py - Entity visualization script
scripts/batch_process.py - Batch processing script
scripts/api_server.py - Flask API server for NER
scripts/advanced_stats.py - Statistics and analytics
scripts/export_data.py - Multi-format export (CSV, TSV, XML, BIO, CoNLL, JSONL)
scripts/evaluate_model.py - Model evaluation with metrics
scripts/load_dataset.py - Dataset loading utilities
Text processing functions - Process and tokenize text documents
Entity tagging functions - Tag entities with BIO labels
Entity extraction functions - Extract entities from text
spaCy model functions - Use spaCy for NER
NLTK model functions - Use NLTK for entity extraction
Transformers functions - Leverage Hugging Face Transformers library
Information extraction functions - Extract entities from documents
BIO format functions - Process BIO format data
Batch processing support - Process multiple documents efficiently
Model evaluation functions - Evaluate model performance
Dataset verification - Data format checking, validation, and quality assessment
Export functionality - Export entities and labels
Error handling - Comprehensive error checking and informative messages
Code comments and documentation - Well-documented code for learning
Complete code examples - Ready-to-run scripts with examples
Modular design - Reusable functions for different NER tasks
Best practices - Follows Python coding standards (PEP 8)
Real-time NER - Real-time entity extraction support

Dataset Features

Comprehensive Named Entity Recognition Dataset with labeled entities in BIO format for NER, information extraction, and NLP applications.

Multiple Entity Types - PERSON, ORG, LOC, DATE, MONEY, PERCENT
Various Entities - Different entity types for comprehensive training
Entity Formats - Various entity formats for real-world applications
BIO Format - Standard BIO tagging format for compatibility
Data Formats - JSON, CSV, BIO formats supported
Organized Structure - Separate files for train, test datasets
Multiple Data Types - Training and test datasets
Entity Organization - Entities organized by type
High-quality Data - Clean, validated, and consistent entity labels
Complete Dataset - Text with corresponding entity labels
Ready for machine learning - Preprocessed data for model training
NER Ready - Pre-labeled data for NER tasks
NER utilities - Pre-built NER functions
Easy to extend dataset - Add more entities or text
Organized project structure - Clear directory organization
Data-based organization - Separate files for train, test datasets
Entity-based annotations - Structured entity and label information
Entity metadata - Entity type and position information
NLP standards - Follows NER best practices
Sample data included - Sample entities and labels
Production ready - Tested and verified NER system

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)
Scikit-learn - Machine learning library (BSD License)
XGBoost - Gradient boosting framework (Apache 2.0)
NumPy - Numerical computing (BSD License)
pandas - Data manipulation (BSD License)
matplotlib - Data Visualization (PSF License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Sameer | 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 Sameer
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Named Entity Recognition Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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