This project focuses on developing a deep learning model for predicting the next word in a given sequence of words using Long Short-Term Memory (LSTM) networks. LSTMs are particularly effective for sequence prediction tasks because they capture long-term dependencies in data. The model is trained on the text of Shakespeare's "Hamlet," providing a rich and complex dataset that challenges the model's predictive capabilities.
The project utilizes advanced deep-learning techniques to enhance natural language processing capabilities, specifically in next-word prediction tasks. By leveraging LSTM networks, it captures intricate patterns and dependencies within text data, ultimately providing a tool for applications such as text auto-completion and interactive chatbots. The deployment through a web application allows for user-friendly interaction with the model, showcasing its practical utility in real-time scenarios.
The project follows a structured workflow consisting of six key steps:
1- Data Collection: We use the text of Shakespeare's "Hamlet" as our dataset. This rich, complex text provides a good challenge for our model.
2- Data Preprocessing: The text data is tokenized, converted into sequences, and padded to ensure uniform input lengths. The sequences are then split into training and testing sets.
3- Model Building: An LSTM model is constructed with an embedding layer, two LSTM layers, and a dense output layer with a softmax activation function to predict the probability of the next word.
4- Model Training: The model is trained using the prepared sequences, with early stopping implemented to prevent overfitting. Early stopping monitors the validation loss and stops training when the loss stops improving.
5- Model Evaluation: The model is evaluated using a set of example sentences to test its ability to predict the next word accurately.
6- Deployment: A Streamlit web application is developed to allow users to input a sequence of words and get the predicted next word in real-time.
Ensure you have the following dependencies installed:
- Python (version 3.11.x || 3.12.x)
- IDE: VS-CODE or collab
- Virtual-environment(venv)
- Other dependencies (refer to the requirement.txt)
You can install the required Python packages using:
pip install -r requirement.txt- Clone the repository:
git clone /~https://github.com/SINGHxTUSHAR/NextWordAI.git
cd NextWordAI- Create a virtual environment (optional but recommended):
python -m venv venv- Activate the virtual environment:
- On Windows:
venv\Scripts\activate
- On macOS/Linux:
source venv/bin/activate
If you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!
If you have any suggestions for me related to this project, feel free to contact me at tusharsinghrawat.delhi@gmail.com or LinkedIn.
This project is licensed under the MIT License - see the LICENSE file for details.