license: mit
datasets:
- lunaopenlabs/LunaAi-dataset
language:
- en
metrics:
- character
base_model:
- lunaopenlabs/LunaAi
new_version: lunaopenlabs/LunaAi
pipeline_tag: text-classification
library_name: adapter-transformers
tags:
- luna
- open
- labs
- LunaAi
- text
- classification
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
- 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/LunaOpenLabs/Luna-Ai.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]].