learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
training_steps: 700
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	19	0.4043	0.0	0.0	0.0	0.9464
No log	2.0	38	0.3303	0.0	0.0	0.0	0.9464
0.7003	3.0	57	0.3030	0.0	0.0	0.0	0.9464
0.7003	4.0	76	0.2785	0.0	0.0	0.0	0.9464
0.7003	5.0	95	0.2136	0.9245	0.1655	0.2808	0.9539
0.275	6.0	114	0.1658	0.5106	0.3243	0.3967	0.9642
0.275	7.0	133	0.1371	0.6326	0.4595	0.5323	0.9714
0.1595	8.0	152	0.1262	0.6312	0.6014	0.6159	0.9750
0.1595	9.0	171	0.1147	0.6520	0.6520	0.6520	0.9767
0.1595	10.0	190	0.1002	0.7273	0.7297	0.7285	0.9798
0.1118	11.0	209	0.0843	0.7450	0.7601	0.7525	0.9812
0.1118	12.0	228	0.0836	0.8102	0.75	0.7789	0.9830
0.1118	13.0	247	0.0780	0.7591	0.8412	0.7981	0.9830
0.0775	14.0	266	0.0713	0.7986	0.7905	0.7946	0.9844
0.0775	15.0	285	0.0711	0.7832	0.8176	0.8	0.9830
0.0599	16.0	304	0.0657	0.8102	0.8074	0.8088	0.9850
0.0599	17.0	323	0.0708	0.8697	0.7669	0.8151	0.9848
0.0599	18.0	342	0.0627	0.7841	0.8345	0.8085	0.9847
0.0503	19.0	361	0.0634	0.8151	0.8041	0.8095	0.9856
0.0503	20.0	380	0.0612	0.7795	0.8480	0.8123	0.9844
0.0503	21.0	399	0.0620	0.7950	0.8514	0.8222	0.9851
0.0422	22.0	418	0.0581	0.8152	0.8345	0.8247	0.9862
0.0422	23.0	437	0.0606	0.8078	0.8378	0.8226	0.9853
0.037	24.0	456	0.0631	0.8215	0.8243	0.8229	0.9853
0.037	25.0	475	0.0673	0.7681	0.8615	0.8121	0.9835
0.037	26.0	494	0.0647	0.7886	0.8446	0.8157	0.9844
0.0326	27.0	513	0.0604	0.8278	0.8446	0.8361	0.9862
0.0326	28.0	532	0.0642	0.8065	0.8446	0.8251	0.9845
0.0305	29.0	551	0.0597	0.8167	0.8581	0.8369	0.9856
0.0305	30.0	570	0.0590	0.8032	0.8547	0.8282	0.9851
0.0305	31.0	589	0.0595	0.8129	0.8514	0.8317	0.9854
0.0277	32.0	608	0.0654	0.8262	0.8514	0.8386	0.9859
0.0277	33.0	627	0.0650	0.8261	0.8345	0.8303	0.9850
0.0277	34.0	646	0.0608	0.8194	0.8581	0.8383	0.9861
0.0264	35.0	665	0.0651	0.8232	0.8649	0.8435	0.9861
0.0264	36.0	684	0.0767	0.8429	0.7973	0.8194	0.9850
0.0252	36.8421	700	0.0578	0.8557	0.8412	0.8484	0.9873

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu118
Datasets 3.4.1
Tokenizers 0.21.1

Giannis17/layoutlmv3-finetuned-invoice_ConControl_v301

layoutlmv3-finetuned-invoice_ConControl_v301

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Giannis17/
layoutlmv3-finetuned-invoice_ConControl_v301