This project fine-tunes the Donut (Large Language and Vision Assistant) model to extract information from invoices. The model is trained to understand and extract key information from invoice images.
invoice_reader/
├── data/ # Directory for invoice images and annotations
│ ├── raw/ # Raw invoice images
│ └── processed/ # Processed and annotated data
├── src/ # Source code
│ ├── data/ # Data processing scripts
│ ├── model/ # Model training and inference code
│ └── utils/ # Utility functions
├── notebooks/ # Jupyter notebooks for experimentation
├── configs/ # Configuration files
├── requirements.txt # Project dependencies
└── README.md # Project documentation
This project includes a .gitignore file to exclude unnecessary files and directories from version control, such as virtual environments, logs, and temporary files. Note that the data/ directory is tracked by git, so any data placed here will be included in version control unless otherwise specified. If you wish to ignore the data/ directory, you can uncomment the relevant line in .gitignore.
- Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies:
pip install -r requirements.txt[To be added as the project develops]
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