https://github.com/programmer-rd-ai/nlprescue
NLPrescue is an advanced Natural Language Processing system designed to detect and classify disaster-related tweets in real-time. Built with PyTorch and modern NLP techniques, it helps emergency responders quickly identify genuine disaster situations on social media platforms.
https://github.com/programmer-rd-ai/nlprescue
deep-learning disaster-detection emergency-response machine-learning natural-language-processing nlp python pytorch social-media-analysis text-classification
Last synced: 3 months ago
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NLPrescue is an advanced Natural Language Processing system designed to detect and classify disaster-related tweets in real-time. Built with PyTorch and modern NLP techniques, it helps emergency responders quickly identify genuine disaster situations on social media platforms.
- Host: GitHub
- URL: https://github.com/programmer-rd-ai/nlprescue
- Owner: Programmer-RD-AI
- License: apache-2.0
- Created: 2023-07-15T06:14:52.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-01T00:10:13.000Z (3 months ago)
- Last Synced: 2025-03-01T01:19:27.442Z (3 months ago)
- Topics: deep-learning, disaster-detection, emergency-response, machine-learning, natural-language-processing, nlp, python, pytorch, social-media-analysis, text-classification
- Language: Python
- Homepage: https://wandb.ai/ranuga-d/NLPrescue/
- Size: 1.34 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 677
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NLPrescue: Intelligent Disaster Tweet Analysis System
A machine learning project that uses Natural Language Processing (NLP) to identify and classify real disaster-related tweets from non-disaster tweets.
## Overview
TweetAlert is an intelligent system that helps emergency responders and disaster management teams quickly identify genuine disaster-related social media content. Using advanced machine learning techniques, it differentiates between tweets about actual emergencies (e.g., "Forest fire spreading near downtown!") and non-emergency tweets using similar language (e.g., "This new album is fire!").
## Project Structure
```
.
├── .github/ # GitHub Actions workflows
├── ML/ # Core ML implementation
│ ├── data/ # Training and test datasets
│ ├── dataset/ # Data loading and processing
│ ├── helper_functions/# Utility functions
│ ├── modelling/ # Model implementations
│ └── predictions/ # Model outputs
├── tests/ # Test suite
└── wandb/ # Weights & Biases logging
```
## Features
- Binary classification of tweets (disaster vs non-disaster)
- PyTorch-based implementation
- Multiple model architectures
- Weights & Biases integration for experiment tracking
- Comprehensive test coverage
- GPU acceleration support
## Requirements
- Python 3.7+
- PyTorch
- torchvision
- torchtext
- pandas
- numpy
- scikit-learn
- wandb
- matplotlib
- tqdm
## Installation
1. Clone the repository
```bash
git clone https://github.com/Programmer-RD-AI/NLP-Disaster-Tweets.git
```
2. Install dependencies
```bash
pip install -r requirements.txt
```
## Usage
To train the model:
```
python run.py
```
Monitor training progress in the Weights & Biases dashboard.
## Dataset
The project uses two main datasets:
- train.csv: Labeled tweets for training
- test.csv: Unlabeled tweets for prediction
Labels:
- 1: Real disaster
- 0: Not a real disaster
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Open a pull request
## License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.