layoutlmv3-finetuned-invoice

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

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	0.0715	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