LSTM Text Classification - A Simple LSTM Example

This project implements a text classification model using Long Short-Term Memory (LSTM) neural networks.

Project Structure

• libraries.py: Contains the import statements for required libraries.
• preprocessing.py: Includes data loading and preprocessing steps.
• model.py: Defines the LSTM model architecture.
• train.py: Contains the code for training the model.
• evaluate.py: Evaluates the trained model on the test data.

How to Run

1. Install the required dependencies:

   pip install -r requirements.txt

2. Run the preprocessing script to prepare the data:

   python preprocessing.py

3. Define and compile the model:

   python model.py

4. Train the model:

   python train.py

5. Evaluate the model:

   python evaluate.py

or

1. Run the run.py script to execute everything in order:

   python run.py

Requirements

The project dependencies are listed in requirements.txt.