Open-source layoutlmv3-finetuned-invoice model - Efficiently extract invoice information, excellent performance on the SROIE dataset

Layoutlmv3 Finetuned Invoice

Developed by ronak1998

An invoice information extraction model fine-tuned based on the LayoutLMv3 architecture, demonstrating outstanding performance on the SROIE dataset

Text Recognition

Transformers

#Invoice Recognition #High-precision OCR #Document Understanding

Downloads 71

Release Time : 6/10/2022

Model Overview

This model is specifically designed to extract key information from invoice documents, such as dates, amounts, merchant details, etc., supporting structured data extraction

Model Features

High-precision Information Extraction

Achieves near-perfect precision (1.0) and recall (0.998) on the SROIE test set

Layout Awareness

Combines text content and document layout information for more accurate entity recognition

End-to-end Processing

Can directly process raw document images without complex preprocessing

Model Capabilities

Invoice Information Extraction

Document Entity Recognition

Structured Data Generation

Use Cases

Financial Automation

Invoice Data Processing

Automatically extracts key fields (amount, date, tax ID, etc.) from scanned invoices

Reduces manual entry errors and improves processing speed by over 90%

Document Management

Document Digitization

Converts paper invoices into structured data

Enables searchable and analyzable document content

🚀 layoutlmv3-finetuned-invoice

This model is a fine - tuned version of microsoft/layoutlmv3-base on the sroie dataset, achieving high performance in token classification tasks.

🚀 Quick Start

This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset. It achieves the following results on the evaluation set:

Loss: 0.0030
Precision: 1.0
Recall: 0.9980
F1: 0.9990
Accuracy: 0.9998

📚 Documentation

Datasets

sroie

Metrics

precision
recall
f1
accuracy

Model Index

Property	Details
Model Name	layoutlmv3-finetuned-invoice
Task	Token Classification
Dataset	sroie
Precision	1.0
Recall	0.9979716024340771
F1	0.9989847715736041
Accuracy	0.9997893406361913

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2000

Training Results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.0715	0.972	0.9858	0.9789	0.9971
No log	4.0	200	0.0228	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0174	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0137	0.972	0.9858	0.9789	0.9971
0.1189	10.0	500	0.0122	0.972	0.9858	0.9789	0.9971
0.1189	12.0	600	0.0112	0.972	0.9858	0.9789	0.9971
0.1189	14.0	700	0.0080	0.972	0.9858	0.9789	0.9971
0.1189	16.0	800	0.0100	0.972	0.9858	0.9789	0.9971
0.1189	18.0	900	0.0040	0.9960	0.9980	0.9970	0.9996
0.0097	20.0	1000	0.0030	1.0	0.9980	0.9990	0.9998
0.0097	22.0	1100	0.0028	0.9980	0.9959	0.9970	0.9996
0.0097	24.0	1200	0.0016	1.0	1.0	1.0	1.0
0.0097	26.0	1300	0.0015	1.0	1.0	1.0	1.0
0.0097	28.0	1400	0.0015	0.9980	0.9980	0.9980	0.9998
0.0029	30.0	1500	0.0017	0.9980	0.9980	0.9980	0.9998
0.0029	32.0	1600	0.0026	0.9960	0.9980	0.9970	0.9996
0.0029	34.0	1700	0.0026	0.9960	0.9980	0.9970	0.9996
0.0029	36.0	1800	0.0026	0.9960	0.9980	0.9970	0.9996
0.0029	38.0	1900	0.0025	0.9960	0.9980	0.9970	0.9996
0.002	40.0	2000	0.0026	0.9960	0.9980	0.9970	0.9996

Framework Versions

Transformers 4.20.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1

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