layoutlmv3-finetuned-invoice-2

This model is a fine-tuned version of microsoft/layoutlmv3-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1396
• Precision: 0.7576
• Recall: 0.8929
• F1: 0.8197
• Accuracy: 0.9742

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	4.35	100	0.4241	0.0	0.0	0.0	0.9135
No log	8.7	200	0.2990	0.2353	0.1429	0.1778	0.9239
No log	13.04	300	0.3107	0.5263	0.3571	0.4255	0.9458
No log	17.39	400	0.1345	0.6970	0.8214	0.7541	0.9742
0.2872	21.74	500	0.1396	0.7576	0.8929	0.8197	0.9742
0.2872	26.09	600	0.1673	0.8519	0.8214	0.8364	0.9690
0.2872	30.43	700	0.1784	0.8519	0.8214	0.8364	0.9690
0.2872	34.78	800	0.1401	0.7742	0.8571	0.8136	0.9729
0.2872	39.13	900	0.1480	0.7273	0.8571	0.7869	0.9716
0.0443	43.48	1000	0.1739	0.6970	0.8214	0.7541	0.9703
0.0443	47.83	1100	0.1786	0.7097	0.7857	0.7458	0.9690
0.0443	52.17	1200	0.1832	0.6970	0.8214	0.7541	0.9690
0.0443	56.52	1300	0.1861	0.6389	0.8214	0.7187	0.9690
0.0443	60.87	1400	0.2155	0.6667	0.7143	0.6897	0.9639
0.0198	65.22	1500	0.2087	0.6667	0.7143	0.6897	0.9652
0.0198	69.57	1600	0.1680	0.6970	0.8214	0.7541	0.9703
0.0198	73.91	1700	0.1664	0.6970	0.8214	0.7541	0.9703
0.0198	78.26	1800	0.1795	0.6970	0.8214	0.7541	0.9703
0.0198	82.61	1900	0.1807	0.6970	0.8214	0.7541	0.9703
0.0151	86.96	2000	0.1825	0.6970	0.8214	0.7541	0.9703

Framework versions

• Transformers 4.27.4
• Pytorch 2.0.0+cu117
• Datasets 2.11.0
• Tokenizers 0.13.3