Character-Level Language Modeling using LSTM

This repository contains code for building a Character-Level Language Model using Long Short-Term Memory (LSTM) neural networks. The model is trained on a given text corpus to generate coherent and contextually relevant sequences of characters.

Table of Contents

• Overview
• Dependencies
• Repository Structure
• Usage
• Contributing
• License

Overview

The primary goal of this project is to demonstrate the implementation of an LSTM-based language model for character-level sequence generation. The model is trained on a provided text dataset, and the trained model can be used to generate new sequences of characters.

Dependencies

• Python 3
• PyTorch
• Other dependencies listed in requirements.txt

To install the required dependencies, run:

pip install -r requirements.txt

Repository Structure

• models/: Contains the LSTM model implementation.
• utils/: Includes utility functions for data preprocessing and sequence generation.
• data/: Directory to store training and testing datasets.
• train.py: Script for training the LSTM model.
• generate.py: Script for generating sequences using the trained model.
• README.md: Project documentation.

Usage

1. Clone the repository:

   git clone https://github.com/Kshitij301199/Character_Level_LM_using_LSTM.git
   cd Character_Level_LM_using_LSTM

2. Install dependencies:

   pip install -r requirements.txt

3. Train the model:

   python assignment3.py --default_train 

4. Compare hyperparameters:

   python assignment3.py --custom_train

5. Plot loss for varying learning rates:

   python assignment3.py --plot_loss

6. Compare generated strings for different temperatures:

   python assignment3.py --diff_temp

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests for any improvements or additional features.

License

This project is licensed under the MIT License - see the LICENSE file for details.

This README provides an overview of the repository, information on dependencies, details about the repository structure, usage instructions, guidelines for training and evaluation, an invitation for contributions, and information about the project's license. Customize the content as needed for your specific repository.