Text Classification NLP Project

Project Overview

This project focuses on text classification using Natural Language Processing (NLP) techniques. It includes data preprocessing, tokenization, model training, evaluation, and inference. The project is implemented in Python and uses an LSTM model for classification tasks.

Project Structure

app.py                      # Main application entry point
requirements.txt            # Python dependencies
project_structure.txt       # Project structure description
data/
    train.csv               # Training dataset
    test.csv                # Testing dataset
saved_models/
    lstm_best_model.h5      # Pre-trained LSTM model
src/
    evaluation/             # Evaluation metrics
        metrics.py
    inference/              # Inference logic
        predict.py
    models/                 # Model definitions
        lstm_model.py
    preprocessing/          # Text preprocessing utilities
        text_cleaning.py
    tokenization/           # Tokenization logic
        tokenizer.py
    training/               # Training scripts
        train_lstm.py
    utils/                  # Utility functions
        dataset_loader.py
static/
    style.css               # Static CSS files
templates/
    index.html              # HTML templates

Features

• Data Preprocessing: Cleaning and preparing text data for training.
• Tokenization: Converting text into numerical representations.
• Model Training: Training an LSTM model on the dataset.
• Model Evaluation: Evaluating the model's performance using various metrics.
• Inference: Making predictions on new data.
• Web Interface: A simple web interface for user interaction.

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/Text-Classification-NLP.git

2. Navigate to the project directory:

   cd Text-Classification-NLP

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Train the Model: Run the training script to train the LSTM model:

   python src/training/train_lstm.py

2. Evaluate the Model: Evaluate the trained model using the evaluation script:

   python src/evaluation/metrics.py

3. Run the Application: Start the web application:

   python app.py

   Open your browser and navigate to http://127.0.0.1:5000.

Dependencies

• Python 3.x
• TensorFlow/Keras
• Flask
• Pandas
• NumPy

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

Acknowledgments

• The datasets used in this project.
• Open-source libraries and frameworks.