Loss: 0.6561
Eader: {'precision': 0.3770491803278688, 'recall': 0.27710843373493976, 'f1': 0.3194444444444444, 'number': 83}
Nswer: {'precision': 0.5213675213675214, 'recall': 0.5951219512195122, 'f1': 0.5558086560364465, 'number': 205}
Uestion: {'precision': 0.3722627737226277, 'recall': 0.44155844155844154, 'f1': 0.40396039603960393, 'number': 231}
Overall Precision: 0.4341
Overall Recall: 0.4759
Overall F1: 0.4540
Overall Accuracy: 0.7927

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	Eader	Nswer	Uestion	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.2996	1.0	8	1.0787	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83}	{'precision': 0.0748502994011976, 'recall': 0.24390243902439024, 'f1': 0.11454753722794961, 'number': 205}	{'precision': 0.0704647676161919, 'recall': 0.20346320346320346, 'f1': 0.10467706013363029, 'number': 231}	0.0727	0.1869	0.1046	0.6075
1.0224	2.0	16	0.8740	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83}	{'precision': 0.20823798627002288, 'recall': 0.44390243902439025, 'f1': 0.2834890965732087, 'number': 205}	{'precision': 0.17551963048498845, 'recall': 0.329004329004329, 'f1': 0.2289156626506024, 'number': 231}	0.1920	0.3218	0.2405	0.7001
0.8193	3.0	24	0.6924	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83}	{'precision': 0.310126582278481, 'recall': 0.47804878048780486, 'f1': 0.3761996161228407, 'number': 205}	{'precision': 0.25671641791044775, 'recall': 0.3722943722943723, 'f1': 0.30388692579505294, 'number': 231}	0.2759	0.3545	0.3103	0.7519
0.6764	4.0	32	0.6312	{'precision': 0.14285714285714285, 'recall': 0.04819277108433735, 'f1': 0.07207207207207209, 'number': 83}	{'precision': 0.3914590747330961, 'recall': 0.5365853658536586, 'f1': 0.45267489711934156, 'number': 205}	{'precision': 0.36, 'recall': 0.42857142857142855, 'f1': 0.391304347826087, 'number': 231}	0.3647	0.4104	0.3862	0.7634
0.5456	5.0	40	0.5856	{'precision': 0.3, 'recall': 0.18072289156626506, 'f1': 0.2255639097744361, 'number': 83}	{'precision': 0.42424242424242425, 'recall': 0.5463414634146342, 'f1': 0.47761194029850745, 'number': 205}	{'precision': 0.38022813688212925, 'recall': 0.4329004329004329, 'f1': 0.4048582995951417, 'number': 231}	0.3934	0.4374	0.4142	0.7846
0.4547	6.0	48	0.5899	{'precision': 0.4074074074074074, 'recall': 0.26506024096385544, 'f1': 0.32116788321167883, 'number': 83}	{'precision': 0.44129554655870445, 'recall': 0.5317073170731708, 'f1': 0.4823008849557522, 'number': 205}	{'precision': 0.35094339622641507, 'recall': 0.4025974025974026, 'f1': 0.37500000000000006, 'number': 231}	0.3958	0.4316	0.4129	0.7827
0.3815	7.0	56	0.5921	{'precision': 0.3888888888888889, 'recall': 0.25301204819277107, 'f1': 0.3065693430656934, 'number': 83}	{'precision': 0.483739837398374, 'recall': 0.5804878048780487, 'f1': 0.5277161862527716, 'number': 205}	{'precision': 0.34657039711191334, 'recall': 0.4155844155844156, 'f1': 0.3779527559055118, 'number': 231}	0.4090	0.4547	0.4307	0.7893
0.324	8.0	64	0.5872	{'precision': 0.5, 'recall': 0.26506024096385544, 'f1': 0.3464566929133859, 'number': 83}	{'precision': 0.4957983193277311, 'recall': 0.5756097560975609, 'f1': 0.5327313769751693, 'number': 205}	{'precision': 0.39543726235741444, 'recall': 0.45021645021645024, 'f1': 0.42105263157894735, 'number': 231}	0.4477	0.4701	0.4586	0.7989
0.284	9.0	72	0.6026	{'precision': 0.3968253968253968, 'recall': 0.30120481927710846, 'f1': 0.34246575342465757, 'number': 83}	{'precision': 0.4717741935483871, 'recall': 0.5707317073170731, 'f1': 0.5165562913907285, 'number': 205}	{'precision': 0.35789473684210527, 'recall': 0.44155844155844154, 'f1': 0.39534883720930236, 'number': 231}	0.4094	0.4701	0.4377	0.7897
0.249	10.0	80	0.6137	{'precision': 0.4423076923076923, 'recall': 0.27710843373493976, 'f1': 0.34074074074074073, 'number': 83}	{'precision': 0.5041322314049587, 'recall': 0.5951219512195122, 'f1': 0.5458612975391499, 'number': 205}	{'precision': 0.3607142857142857, 'recall': 0.43722943722943725, 'f1': 0.39530332681017616, 'number': 231}	0.4286	0.4740	0.4501	0.7981
0.2288	11.0	88	0.6367	{'precision': 0.38571428571428573, 'recall': 0.3253012048192771, 'f1': 0.35294117647058826, 'number': 83}	{'precision': 0.48717948717948717, 'recall': 0.5560975609756098, 'f1': 0.5193621867881548, 'number': 205}	{'precision': 0.38267148014440433, 'recall': 0.4588744588744589, 'f1': 0.4173228346456693, 'number': 231}	0.4251	0.4759	0.4491	0.7912
0.2031	12.0	96	0.6401	{'precision': 0.46, 'recall': 0.27710843373493976, 'f1': 0.3458646616541353, 'number': 83}	{'precision': 0.497907949790795, 'recall': 0.5804878048780487, 'f1': 0.536036036036036, 'number': 205}	{'precision': 0.3800738007380074, 'recall': 0.4458874458874459, 'f1': 0.4103585657370518, 'number': 231}	0.4375	0.4721	0.4541	0.7985
0.193	13.0	104	0.6539	{'precision': 0.37142857142857144, 'recall': 0.3132530120481928, 'f1': 0.33986928104575165, 'number': 83}	{'precision': 0.5321100917431193, 'recall': 0.5658536585365853, 'f1': 0.5484633569739952, 'number': 205}	{'precision': 0.3969465648854962, 'recall': 0.45021645021645024, 'f1': 0.4219066937119675, 'number': 231}	0.4473	0.4740	0.4602	0.7904
0.1895	14.0	112	0.6557	{'precision': 0.39344262295081966, 'recall': 0.2891566265060241, 'f1': 0.3333333333333333, 'number': 83}	{'precision': 0.5429864253393665, 'recall': 0.5853658536585366, 'f1': 0.5633802816901408, 'number': 205}	{'precision': 0.3916349809885932, 'recall': 0.4458874458874459, 'f1': 0.41700404858299595, 'number': 231}	0.4532	0.4759	0.4643	0.7904
0.1775	15.0	120	0.6561	{'precision': 0.3770491803278688, 'recall': 0.27710843373493976, 'f1': 0.3194444444444444, 'number': 83}	{'precision': 0.5213675213675214, 'recall': 0.5951219512195122, 'f1': 0.5558086560364465, 'number': 205}	{'precision': 0.3722627737226277, 'recall': 0.44155844155844154, 'f1': 0.40396039603960393, 'number': 231}	0.4341	0.4759	0.4540	0.7927

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

pabloma09/layoutlm-FUNSD-only-1fold

layoutlm-FUNSD-only-1fold

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

pabloma09/
layoutlm-FUNSD-only-1fold