Loss: 1.0554
Answer: {'precision': 0.4105691056910569, 'recall': 0.49938195302843014, 'f1': 0.45064138315672064, 'number': 809}
Header: {'precision': 0.36470588235294116, 'recall': 0.2605042016806723, 'f1': 0.30392156862745096, 'number': 119}
Question: {'precision': 0.48371104815864024, 'recall': 0.6413145539906103, 'f1': 0.551473556721841, 'number': 1065}
Overall Precision: 0.4506
Overall Recall: 0.5610
Overall F1: 0.4998
Overall Accuracy: 0.6149

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7702	1.0	10	1.5768	{'precision': 0.040923399790136414, 'recall': 0.048207663782447466, 'f1': 0.04426787741203178, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.38510301109350237, 'recall': 0.22816901408450704, 'f1': 0.28655660377358494, 'number': 1065}	0.1780	0.1415	0.1577	0.3540
1.4963	2.0	20	1.4062	{'precision': 0.1941294196130754, 'recall': 0.35970333745364647, 'f1': 0.2521663778162912, 'number': 809}	{'precision': 0.03571428571428571, 'recall': 0.01680672268907563, 'f1': 0.022857142857142857, 'number': 119}	{'precision': 0.28505291005291006, 'recall': 0.40469483568075115, 'f1': 0.33449747768723326, 'number': 1065}	0.2361	0.3633	0.2862	0.4204
1.2983	3.0	30	1.2020	{'precision': 0.23365122615803816, 'recall': 0.42398022249690975, 'f1': 0.3012736056214317, 'number': 809}	{'precision': 0.13846153846153847, 'recall': 0.07563025210084033, 'f1': 0.09782608695652173, 'number': 119}	{'precision': 0.3307776560788609, 'recall': 0.5671361502347417, 'f1': 0.4178484953303355, 'number': 1065}	0.2846	0.4797	0.3572	0.4806
1.1603	4.0	40	1.1227	{'precision': 0.2243436754176611, 'recall': 0.34857849196538937, 'f1': 0.2729912875121007, 'number': 809}	{'precision': 0.2222222222222222, 'recall': 0.18487394957983194, 'f1': 0.2018348623853211, 'number': 119}	{'precision': 0.35071846726982436, 'recall': 0.6187793427230047, 'f1': 0.4476902173913044, 'number': 1065}	0.2977	0.4832	0.3684	0.5265
1.0771	5.0	50	1.0953	{'precision': 0.2655198204936425, 'recall': 0.4388133498145859, 'f1': 0.33084808946877914, 'number': 809}	{'precision': 0.26666666666666666, 'recall': 0.20168067226890757, 'f1': 0.22966507177033493, 'number': 119}	{'precision': 0.3632745878339966, 'recall': 0.6, 'f1': 0.45254957507082155, 'number': 1065}	0.3195	0.5108	0.3931	0.5453
1.0102	6.0	60	1.0388	{'precision': 0.30492285084496695, 'recall': 0.5129789864029666, 'f1': 0.3824884792626728, 'number': 809}	{'precision': 0.3283582089552239, 'recall': 0.18487394957983194, 'f1': 0.2365591397849462, 'number': 119}	{'precision': 0.4519543973941368, 'recall': 0.5211267605633803, 'f1': 0.4840819886611426, 'number': 1065}	0.3735	0.4977	0.4268	0.5839
0.9312	7.0	70	1.0265	{'precision': 0.32556131260794474, 'recall': 0.46600741656365885, 'f1': 0.3833248601931876, 'number': 809}	{'precision': 0.2828282828282828, 'recall': 0.23529411764705882, 'f1': 0.25688073394495414, 'number': 119}	{'precision': 0.47326007326007324, 'recall': 0.6065727699530516, 'f1': 0.5316872427983539, 'number': 1065}	0.4008	0.5273	0.4555	0.5969
0.8732	8.0	80	1.0508	{'precision': 0.33681073025335323, 'recall': 0.5587144622991347, 'f1': 0.4202696420269642, 'number': 809}	{'precision': 0.3561643835616438, 'recall': 0.2184873949579832, 'f1': 0.2708333333333333, 'number': 119}	{'precision': 0.4556126192223037, 'recall': 0.5830985915492958, 'f1': 0.5115321252059308, 'number': 1065}	0.3956	0.5514	0.4607	0.5947
0.808	9.0	90	1.0282	{'precision': 0.36807511737089205, 'recall': 0.484548825710754, 'f1': 0.41835645677694777, 'number': 809}	{'precision': 0.3058823529411765, 'recall': 0.2184873949579832, 'f1': 0.2549019607843137, 'number': 119}	{'precision': 0.46965317919075145, 'recall': 0.6103286384976526, 'f1': 0.5308289097590853, 'number': 1065}	0.4215	0.5359	0.4718	0.6085
0.7928	10.0	100	1.0475	{'precision': 0.38683498647430115, 'recall': 0.5302843016069221, 'f1': 0.44734098018769547, 'number': 809}	{'precision': 0.36363636363636365, 'recall': 0.23529411764705882, 'f1': 0.2857142857142857, 'number': 119}	{'precision': 0.49149922720247297, 'recall': 0.5971830985915493, 'f1': 0.5392115303094531, 'number': 1065}	0.4407	0.5484	0.4887	0.6054
0.7164	11.0	110	1.0310	{'precision': 0.38894472361809046, 'recall': 0.4783683559950556, 'f1': 0.4290465631929047, 'number': 809}	{'precision': 0.38961038961038963, 'recall': 0.25210084033613445, 'f1': 0.30612244897959184, 'number': 119}	{'precision': 0.4831223628691983, 'recall': 0.6450704225352113, 'f1': 0.5524728588661038, 'number': 1065}	0.4427	0.5539	0.4921	0.6149
0.707	12.0	120	1.0295	{'precision': 0.40441176470588236, 'recall': 0.4758961681087763, 'f1': 0.43725156161272005, 'number': 809}	{'precision': 0.3655913978494624, 'recall': 0.2857142857142857, 'f1': 0.32075471698113206, 'number': 119}	{'precision': 0.4713031735313977, 'recall': 0.6553990610328638, 'f1': 0.5483110761979575, 'number': 1065}	0.4422	0.5605	0.4944	0.6172
0.6765	13.0	130	1.0494	{'precision': 0.4107485604606526, 'recall': 0.5290482076637825, 'f1': 0.46245272825499734, 'number': 809}	{'precision': 0.4305555555555556, 'recall': 0.2605042016806723, 'f1': 0.324607329842932, 'number': 119}	{'precision': 0.4879825200291333, 'recall': 0.6291079812206573, 'f1': 0.5496308449548811, 'number': 1065}	0.4540	0.5665	0.5040	0.6156
0.6489	14.0	140	1.0557	{'precision': 0.4165009940357853, 'recall': 0.5179233621755254, 'f1': 0.4617079889807163, 'number': 809}	{'precision': 0.4, 'recall': 0.2689075630252101, 'f1': 0.32160804020100503, 'number': 119}	{'precision': 0.4891304347826087, 'recall': 0.6338028169014085, 'f1': 0.5521472392638037, 'number': 1065}	0.4566	0.5650	0.5050	0.6142
0.6397	15.0	150	1.0554	{'precision': 0.4105691056910569, 'recall': 0.49938195302843014, 'f1': 0.45064138315672064, 'number': 809}	{'precision': 0.36470588235294116, 'recall': 0.2605042016806723, 'f1': 0.30392156862745096, 'number': 119}	{'precision': 0.48371104815864024, 'recall': 0.6413145539906103, 'f1': 0.551473556721841, 'number': 1065}	0.4506	0.5610	0.4998	0.6149

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

marcoCasamento/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

marcoCasamento/
layoutlm-funsd