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