Llama3 Fine-Tuned Model for Invoice Line Item Data Extraction
This repository contains a fine-tuned version of the Llama3 model, specifically optimized for extracting line item data from tables in invoices. This model is designed for automating data extraction tasks commonly encountered in financial document processing, such as invoicing, auditing, and bookkeeping.
Model Overview
Extracting structured line item data from invoices can be challenging due to varying formats and layouts. This model, fine-tuned on invoice data, addresses these challenges by accurately identifying and extracting key fields related to line items, including product descriptions, quantities, unit prices, tax rates, and total amounts.
Key Features
- Optimized for Financial Documents: Fine-tuned for invoice data, capable of accurately identifying line item details.
- Comprehensive Field Extraction: Extracts essential fields such as:
- Description
- Quantity
- Unit Price
- Tax Rates (SGST, CGST, IGST)
- Total Amount
- Flexible with Multiple Formats: Compatible with diverse invoice formats, making it ideal for multi-vendor data processing.
Usage
Requirements
To use this model, you will need to have:
- Python 3.6 or later
- Hugging Face Transformers library
- PyTorch library
Installation
Install the necessary libraries with pip:
1pip install transformers torch
2
