Theivaprakasham/layoutlmv3-finetuned-invoice

LayoutLM-v3 model fine-tuned on invoice dataset

This model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice dataset.

We use Microsoft’s LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds.

It achieves the following results on the evaluation set:

• Loss: 0.0012
• Precision: 1.0
• Recall: 1.0
• F1: 1.0
• Accuracy: 1.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

All the training codes are available from the below GitHub link.

https://github.com/Theivaprakasham/layoutlmv3

The model can be evaluated at the HuggingFace Spaces link:

https://huggingface.co/spaces/Theivaprakasham/layoutlmv3_invoice

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	0.0878	0.968	0.9817	0.9748	0.9966
No log	4.0	200	0.0241	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0186	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0184	0.9854	0.9574	0.9712	0.9956
0.1308	10.0	500	0.0121	0.972	0.9858	0.9789	0.9971
0.1308	12.0	600	0.0076	0.9939	0.9878	0.9908	0.9987
0.1308	14.0	700	0.0047	1.0	0.9959	0.9980	0.9996
0.1308	16.0	800	0.0036	0.9960	0.9980	0.9970	0.9996
0.1308	18.0	900	0.0045	0.9960	0.9980	0.9970	0.9996
0.0069	20.0	1000	0.0043	0.9960	0.9980	0.9970	0.9996
0.0069	22.0	1100	0.0016	1.0	1.0	1.0	1.0
0.0069	24.0	1200	0.0015	1.0	1.0	1.0	1.0
0.0069	26.0	1300	0.0014	1.0	1.0	1.0	1.0
0.0069	28.0	1400	0.0013	1.0	1.0	1.0	1.0
0.0026	30.0	1500	0.0012	1.0	1.0	1.0	1.0
0.0026	32.0	1600	0.0012	1.0	1.0	1.0	1.0
0.0026	34.0	1700	0.0011	1.0	1.0	1.0	1.0
0.0026	36.0	1800	0.0011	1.0	1.0	1.0	1.0
0.0026	38.0	1900	0.0011	1.0	1.0	1.0	1.0
0.002	40.0	2000	0.0011	1.0	1.0	1.0	1.0

Framework versions

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