SatyamSingh8306/Next-Word-Predictor

LSTM Model for Predicting the Next Word

Welcome to the Next Word Prediction Model repository! This project implements a Long Short-Term Memory (LSTM) neural network to predict the next word in a sequence of text. The model is designed to enhance text-based applications, such as chatbots, autocomplete systems, and text editors.

📌 Features

• Deep Learning Architecture: Built using LSTM layers for effective sequence modeling.
• Training with Large Datasets: Trained on large text corpora to ensure high accuracy.
• Customizable: Easily retrainable with your own dataset.
• Real-Time Predictions: Optimized for quick and efficient word prediction.

🚀 Getting Started

Follow these steps to set up and run the model:

Prerequisites

Ensure you have Python installed along with the following libraries:

pip install tensorflow numpy pandas nltk

Installation

Clone this repository to your local machine:

git clone https://github.com/your-username/lstm-next-word-prediction.git
cd lstm-next-word-prediction

Dataset Preparation

1. Provide a text dataset for training (e.g., books, articles, or conversations).
2. Place the dataset file in the data/ folder and name it dataset.txt.
3. The preprocessing script will tokenize and prepare the text for training.

Model Training

Train the LSTM model by running:

python train_model.py

This script will:

• Preprocess the dataset
• Train the LSTM model
• Save the trained model in the model/ directory

Predicting the Next Word

After training, use the model to predict the next word in a sequence:

python predict_next_word.py "The quick brown"

Example output:

Predicted next word: fox

📂 Project Structure

.
├── data
│   └── dataset.txt            # Input dataset
├── model
│   └── lstm_model.h5          # Trained LSTM model
├── train_model.py             # Training script
├── predict_next_word.py       # Prediction script
├── requirements.txt           # Dependencies
├── README.md                  # Project documentation

🛠️ How It Works

1. Data Preprocessing:

   • Tokenizes the input text.
   • Creates sequences of words.
   • Converts sequences into numerical format for model training.

2. LSTM Model:

   • Uses TensorFlow/Keras to build a sequential LSTM model.
   • Learns word dependencies to predict the next word.

3. Prediction:

   • Accepts a string of words as input.
   • Outputs the most probable next word.

🧪 Testing

You can test the model using a pre-trained example or your custom-trained model. Use the predict_next_word.py script to evaluate real-time predictions.

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch-name).
3. Commit your changes (git commit -m "Add some feature").
4. Push to the branch (git push origin feature-branch-name).
5. Open a Pull Request.

📖 References

• TensorFlow Documentation: https://www.tensorflow.org/
• Keras Sequential API: https://keras.io/guides/sequential_model/

📧 Contact

For any inquiries, please reach out to:

• Satyam Singh
• Email: [satyamsingh7734@gmail.com]

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