Loss: 0.0293
Ddress: {'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}
Eller: {'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}
Imestamp: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}
Otal Cost: {'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}
Overall Precision: 0.9909
Overall Recall: 0.992
Overall F1: 0.9914
Overall Accuracy: 0.9960

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Ddress	Eller	Imestamp	Otal Cost	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.3063	1.0	55	0.0304	{'precision': 0.9585253456221198, 'recall': 0.9585253456221198, 'f1': 0.9585253456221198, 'number': 217}	{'precision': 0.991304347826087, 'recall': 0.9743589743589743, 'f1': 0.9827586206896551, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.986046511627907, 'recall': 0.9953051643192489, 'f1': 0.9906542056074766, 'number': 213}	0.9828	0.9806	0.9817	0.9912
0.0332	2.0	110	0.0303	{'precision': 0.967741935483871, 'recall': 0.967741935483871, 'f1': 0.967741935483871, 'number': 217}	{'precision': 0.991304347826087, 'recall': 0.9743589743589743, 'f1': 0.9827586206896551, 'number': 234}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}	{'precision': 0.9906976744186047, 'recall': 1.0, 'f1': 0.9953271028037384, 'number': 213}	0.9874	0.9851	0.9863	0.9928
0.0174	3.0	165	0.0252	{'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217}	{'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213}	0.9863	0.9886	0.9874	0.9944
0.0145	4.0	220	0.0271	{'precision': 0.967741935483871, 'recall': 0.967741935483871, 'f1': 0.967741935483871, 'number': 217}	{'precision': 0.9913793103448276, 'recall': 0.9829059829059829, 'f1': 0.9871244635193134, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213}	0.9863	0.9851	0.9857	0.9936
0.0114	5.0	275	0.0254	{'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}	{'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213}	0.9886	0.9897	0.9891	0.9952
0.0079	6.0	330	0.0273	{'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217}	{'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213}	0.9863	0.9886	0.9874	0.9944
0.0053	7.0	385	0.0259	{'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}	{'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}	{'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}	0.9909	0.992	0.9914	0.9960
0.005	8.0	440	0.0255	{'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217}	{'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213}	0.9863	0.9886	0.9874	0.9944
0.0034	9.0	495	0.0281	{'precision': 0.9768518518518519, 'recall': 0.9723502304147466, 'f1': 0.97459584295612, 'number': 217}	{'precision': 0.9872340425531915, 'recall': 0.9914529914529915, 'f1': 0.9893390191897654, 'number': 234}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}	{'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}	0.9897	0.9909	0.9903	0.9952
0.0032	10.0	550	0.0290	{'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217}	{'precision': 0.9914163090128756, 'recall': 0.9871794871794872, 'f1': 0.9892933618843683, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.9906542056074766, 'recall': 0.9953051643192489, 'f1': 0.9929742388758782, 'number': 213}	0.9874	0.9874	0.9874	0.9944
0.0032	11.0	605	0.0306	{'precision': 0.9723502304147466, 'recall': 0.9723502304147466, 'f1': 0.9723502304147466, 'number': 217}	{'precision': 0.9913793103448276, 'recall': 0.9829059829059829, 'f1': 0.9871244635193134, 'number': 234}	{'precision': 0.995260663507109, 'recall': 0.995260663507109, 'f1': 0.995260663507109, 'number': 211}	{'precision': 0.986046511627907, 'recall': 0.9953051643192489, 'f1': 0.9906542056074766, 'number': 213}	0.9863	0.9863	0.9863	0.9936
0.0018	12.0	660	0.0273	{'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}	{'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}	{'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}	0.9909	0.992	0.9914	0.9960
0.0007	13.0	715	0.0266	{'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}	{'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}	{'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}	0.9909	0.992	0.9914	0.9960
0.0006	14.0	770	0.0292	{'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}	{'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}	{'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}	0.9909	0.992	0.9914	0.9960
0.0006	15.0	825	0.0293	{'precision': 0.9769585253456221, 'recall': 0.9769585253456221, 'f1': 0.9769585253456222, 'number': 217}	{'precision': 0.9914529914529915, 'recall': 0.9914529914529915, 'f1': 0.9914529914529915, 'number': 234}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 211}	{'precision': 0.9953271028037384, 'recall': 1.0, 'f1': 0.9976580796252927, 'number': 213}	0.9909	0.992	0.9914	0.9960

Framework versions

Transformers 4.46.3
Pytorch 2.4.0
Datasets 3.1.0
Tokenizers 0.20.3

HungNguyen142/layoutlm-mcocr

layoutlm-mcocr

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

HungNguyen142/
layoutlm-mcocr