Text_Processing_RNN_LSTM

Text_Processing_RNN_LSTM leverages RNN and LSTM models for advanced text processing. It features deep learning techniques for NLP tasks, utilizing GloVe for word embeddings, aimed at both educational and practical applications. The sophisticated text processing framework that utilizes Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models. Designed to tackle various NLP tasks, this project demonstrates the power of deep learning in understanding and generating text. By incorporating GloVe word embeddings, DeepTextRNN enhances the semantic analysis of text, making it a valuable resource for researchers and developers alike.

Features

• RNN and LSTM Implementation: Harnesses the sequential nature of text for accurate processing and prediction.
• GloVe Word Embeddings: Utilizes GloVe for improved word representation, enriching the model's understanding of language nuances.
• Text Data Preprocessing: Offers tools for efficiently preparing text data for model training.
• Model Training and Evaluation: Demonstrates the end-to-end process of training deep learning models on text data and evaluating their performance.

Getting Started

Prerequisites

Ensure you have Python 3.8 or newer installed. It's also recommended to use a virtual environment:

python -m venv deepenv
source deepenv/bin/activate  # On Windows use `deepenv\Scripts\activate`

Installation

Clone the repository and install the dependencies:

git clone https://github.com/<your-username>/DeepTextRNN.git
cd DeepTextRNN
pip install -r requirements.txt

Running the Project

Launch Jupyter Notebook to access and run the notebooks:

jupyter notebook

Navigate to the project notebooks and run them to explore DeepTextRNN's capabilities.

Contributing

We welcome contributions to DeepTextRNN! If you have suggestions for improvements or new features, please feel free to:

1. Fork the project.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -am 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a pull request.

Please ensure your code adheres to the project's coding standards.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• Thanks to the creators of GloVe for providing pre-trained word embeddings.
• Appreciation to the open-source community for the continuous inspiration and support.