Next Word Predictor with LSTM Neural Network A next word predictor project implemented using LSTM neural networks for natural language processing. This project is designed to predict the next word in a given sequence of words, making it suitable for applications like autocomplete, language modeling, and more.
Project Demo: Enter your word: this output: your work
Table of Contents Overview Installation Usage Model Architecture Dataset Contributing License Overview The Next Word Predictor with LSTM Neural Network is built to provide accurate and efficient word predictions based on the context of the previous words in a sentence or text. This project aims to help developers integrate advanced text prediction capabilities into their applications.
Installation To set up the project locally, follow these steps:
Clone the repository:
git clone https://github.com/your-username/next-word-predictor.git Install the required dependencies:scikit-learn,tensorflow,nltk,etc. Please refer to the project imports for more...
pip install -r requirements.txt Usage To use the Next Word Predictor, follow these steps:
Open the Jupyter Notebook or Python script provided in the project.
Load the pre-trained LSTM model using your preferred method.
Input a sequence of words or a sentence to predict the next word.
Obtain the predicted word or a list of suggestions based on the context.
Model Architecture The Next Word Predictor project utilizes a Long Short-Term Memory (LSTM) neural network for its prediction tasks. The LSTM architecture is well-suited for handling sequential data and capturing long-range dependencies in the input text.
Here's an overview of the model architecture:
Input Layer LSTM Layers (with adjustable units) Output Layer with Softmax Activation Training with Backpropagation Through Time (BPTT) Dataset The LSTM model is trained on a large and diverse text corpus. If you wish to use a different dataset, you can replace the training data according to your requirements.
Contributing We welcome contributions to enhance and improve this project. Feel free to open issues, create pull requests, and discuss potential improvements.
License This project is licensed under the MIT License - see the LICENSE file for details.