layoutlm invoices

Introduction

LAYOUTLM-INVOICES is a fine-tuned version of the multi-modal LayoutLM model designed for document question answering, specifically on invoices and similar documents. It is trained using a proprietary dataset along with public datasets such as SQuAD2.0 and DocVQA to enhance its ability to comprehend and extract information from documents.

Architecture

This model leverages the LayoutLM architecture, which is a multi-modal framework that processes both text and visual elements from documents. It incorporates an additional classifier head that enables it to predict non-consecutive tokens, a common limitation in other question-answering models.

Training

LAYOUTLM-INVOICES has been fine-tuned on various datasets to improve its document comprehension capabilities. The proprietary dataset of invoices and public datasets like SQuAD2.0 and DocVQA contribute to its understanding of both specific and general document structures.

Guide: Running Locally

To use the LAYOUTLM-INVOICES model locally, follow these steps:

1. Install Dependencies: Ensure that Python and necessary libraries, such as Hugging Face's Transformers, are installed.
2. Clone the Repository: Obtain the model from the Hugging Face Model Hub.
3. Load the Model: Use the Hugging Face Transformers library to load the model.
4. Prepare Input Data: Format your document data according to the model’s requirements.
5. Run Inference: Execute the model to extract desired information from documents.

For optimal performance, it's recommended to use cloud GPUs such as those available on AWS or Google Cloud Platform.

License

The LAYOUTLM-INVOICES model is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (cc-by-nc-sa-4.0). This allows for sharing and adapting the model non-commercially, as long as appropriate credit is given and any derivatives are licensed under identical terms.