GPU-Accelerated Next Word Prediction Using LSTM and PyTorch 🚀

Welcome to the GPU-Accelerated Next Word Prediction Using LSTM and PyTorch repository! This project showcases an efficient model for predicting the next word in a sequence using Long Short-Term Memory (LSTM) networks. By leveraging the power of GPUs and the PyTorch framework, this implementation aims to enhance the speed and accuracy of next-word predictions.

Table of Contents

• Introduction
• Features
• Getting Started
  • Prerequisites
  • Installation
  • Data Preparation
• Model Architecture
• Training the Model
• Generating Predictions
• Usage
• Contributing
• License
• Contact
• Releases

Introduction

In the age of digital communication, next-word prediction plays a crucial role in enhancing user experience. This repository implements a next-word prediction model using LSTM networks, optimized for GPU processing. The model takes a sequence of words as input and predicts the most likely next word based on the context provided.

Features

• GPU Acceleration: Utilizes GPU for faster training and prediction.
• LSTM Architecture: Implements Long Short-Term Memory networks for effective sequence modeling.
• Data Preprocessing: Includes tokenization and vocabulary creation using NLTK.
• Text Generation: Generates coherent text predictions based on input phrases.
• Easy to Use: Simple API for generating predictions.

Getting Started

Prerequisites

To run this project, you need the following software installed on your machine:

• Python 3.6 or higher
• PyTorch (with GPU support)
• NLTK
• Other dependencies listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/sebastian2005-RP/GPU-Accelerated-Next-Word-Prediction-Using-LSTM-and-PyTorch.git
   cd GPU-Accelerated-Next-Word-Prediction-Using-LSTM-and-PyTorch

2. Install the required packages:

   pip install -r requirements.txt

Data Preparation

Before training the model, you need to prepare your dataset. This involves tokenizing your text and creating a vocabulary. You can use the provided scripts in the data_preprocessing folder.

1. Place your text data in the data folder.

2. Run the data preprocessing script:

   python data_preprocessing/preprocess.py

This will generate the necessary tokenized data and vocabulary files.

Model Architecture

The model architecture consists of an LSTM layer followed by a fully connected layer. This setup allows the model to learn dependencies over long sequences of text.

LSTM Layer

• Input Size: The size of the vocabulary.
• Hidden Size: Number of features in the hidden state.
• Number of Layers: Stacked LSTM layers for better learning.

Fully Connected Layer

• Takes the output from the LSTM layer and predicts the next word based on the learned features.

Training the Model

To train the model, use the following command:

python train.py --epochs 10 --batch_size 64

Adjust the epochs and batch_size parameters as needed. The model will save the trained weights to the models directory.

Generating Predictions

Once the model is trained, you can generate predictions using the provided script:

python predict.py --input "Your input phrase here"

This will output the predicted next word based on the input phrase.

Usage

To utilize the model in your applications, follow these steps:

1. Load the trained model.
2. Preprocess your input text.
3. Call the prediction function.

Example

import torch
from model import LSTMModel

# Load the model
model = LSTMModel()
model.load_state_dict(torch.load('models/model_weights.pth'))

# Prepare input
input_text = "The quick brown fox"
# Generate prediction
predicted_word = model.predict(input_text)
print(predicted_word)

Contributing

Contributions are welcome! If you have suggestions for improvements or features, feel free to open an issue or submit a pull request.

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

License

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

Contact

For questions or suggestions, you can reach out to the maintainer:

• Name: Sebastian
• Email: [email protected]

Releases

To download the latest release of this project, visit the Releases section.

You can also check for updates and new features regularly.

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Thank you for checking out this project! We hope it helps you in your journey of exploring next-word prediction using deep learning techniques. Happy coding!