Next Word Predictor using LSTM

This repository contains code and resources for building a next word prediction model using Long Short-Term Memory (LSTM) neural networks. The project aims to create a predictive text system that suggests the most likely word to follow a given sequence of words. This type of technology has a wide range of applications, from text composition assistance to natural language interfaces.

Features

• LSTM-Based Model: Utilizes an LSTM architecture to analyze sequences of words and predict the most likely next word.
• Customizable Vocabulary: Allows for training with different text corpora, enabling flexibility in application contexts.
• Interactive Prediction: Includes a script for interactive text prediction, allowing users to test the model with custom inputs.

Getting Started

Prerequisites

• Python 3.7+
• tensorflow and numpy libraries

Code

For detailed code go here [https://www.kaggle.com/code/harshgupta2003/next-word-predictor-using-lstm-rnn-gru]

Contributing

Contributions are welcome! If you'd like to contribute to the project, please fork the repository and submit a pull request. You can also report issues or suggest features via the GitHub issue tracker.

Acknowledgments

Special thanks to the developers of TensorFlow for the LSTM framework and to the contributors who helped make this project possible.