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+# Luna AI
+
+Luna AI is an open-source AI model developed by Luna OpenLabs for text classification tasks. Leveraging the BERT architecture, this model is designed to classify text into predefined categories efficiently and accurately.
+
+## Table of Contents
+
+- [Features](#features)
+- [Installation](#installation)
+- [Dataset](#dataset)
+- [Usage](#usage)
+  - [Training the Model](#training-the-model)
+  - [Saving and Loading the Model](#saving-and-loading-the-model)
+  - [Testing the Model](#testing-the-model)
+- [Contributing](#contributing)
+- [License](#license)
+- [Contact](#contact)
+
+## Features
+
+- **Text Classification**: Classify text data into various categories.
+- **Built on BERT**: Utilizes the powerful BERT architecture for natural language understanding.
+- **Easy Integration**: Works seamlessly with Hugging Face Transformers library.
+- **Open Source**: Available for anyone to use, modify, and distribute.
+
+## Installation
+
+### Prerequisites
+
+- Python 3.7 or higher
+- pip (Python package installer)
+
+### Clone the Repository
+
+To clone the repository, run the following command:
+
+bash
+git clone https://github.com/yourusername/LunaAI.git
+
+### Install Requirements
+To install the required packages, use:
+
+bash
+pip install -r requirements.txt
+
+### Dataset
+Luna AI requires a dataset in CSV format with two columns: text and label. An example dataset is provided in the data/ directory.
+
+### Example Dataset Structure
+Here’s an example of how the dataset should be structured:
+
+csv
+text,label
+"I love this product!",1
+"This is the worst experience.",0
+
+### Usage
+Training the Model
+
+To train the model, execute the following command:
+
+bash
+python training/train.py
+
+This command will load the dataset from data/dataset.csv and initiate the training process.
+
+### Saving and Loading the Model
+After training, save the trained model using:
+
+bash
+python save_model.py
+
+This will save the model and its tokenizer in the luna_ai_model directory.
+
+### Testing the Model
+To test the model with sample inputs, you can use the test_model.py script. Modify the sample_text variable in the script as needed.
+
+### Run the test script with:
+
+bash
+python test_model.py
+
+### Example Output
+The model will output the predicted class for the provided sample text.
+
+### Contributing
+Contributions are welcome! If you have suggestions, improvements, or bug fixes, please follow these steps:
+
+Fork the repository.
+Create a new branch (git checkout -b feature-branch).
+Make your changes and commit them (git commit -m 'Add some feature').
+Push to the branch (git push origin feature-branch).
+Open a pull request.
+
+### License
+This project is licensed under the MIT License. See the LICENSE file for details.
+
+### Contact
+For questions, suggestions, or feedback, feel free to contact the Luna OpenLabs team at [[email protected]].