layoutlmv3-finetuned-invoice

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

• Loss: 0.0042
• Precision: 0.9960
• Recall: 0.9980
• F1: 0.9970
• Accuracy: 0.9996

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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	0.1164	0.902	0.9148	0.9084	0.9897
No log	4.0	200	0.0262	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0157	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0097	0.9877	0.9797	0.9837	0.9979
0.1294	10.0	500	0.0065	0.9939	0.9959	0.9949	0.9994
0.1294	12.0	600	0.0042	0.9960	0.9980	0.9970	0.9996
0.1294	14.0	700	0.0048	0.9960	0.9980	0.9970	0.9996
0.1294	16.0	800	0.0047	0.9960	0.9980	0.9970	0.9996
0.1294	18.0	900	0.0045	0.9960	0.9980	0.9970	0.9996
0.0051	20.0	1000	0.0042	0.9960	0.9980	0.9970	0.9996
0.0051	22.0	1100	0.0041	0.9960	0.9980	0.9970	0.9996
0.0051	24.0	1200	0.0041	0.9960	0.9980	0.9970	0.9996
0.0051	26.0	1300	0.0040	0.9960	0.9980	0.9970	0.9996
0.0051	28.0	1400	0.0040	0.9960	0.9980	0.9970	0.9996
0.0024	30.0	1500	0.0040	0.9960	0.9980	0.9970	0.9996
0.0024	32.0	1600	0.0040	0.9960	0.9980	0.9970	0.9996
0.0024	34.0	1700	0.0039	0.9960	0.9980	0.9970	0.9996
0.0024	36.0	1800	0.0039	0.9960	0.9980	0.9970	0.9996
0.0024	38.0	1900	0.0039	0.9960	0.9980	0.9970	0.9996
0.0018	40.0	2000	0.0039	0.9960	0.9980	0.9970	0.9996

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1