Loss: 0.6849
Answer: {'precision': 0.7012987012987013, 'recall': 0.8009888751545118, 'f1': 0.7478361223312175, 'number': 809}
Header: {'precision': 0.2949640287769784, 'recall': 0.3445378151260504, 'f1': 0.31782945736434104, 'number': 119}
Question: {'precision': 0.7841918294849023, 'recall': 0.8291079812206573, 'f1': 0.8060246462802373, 'number': 1065}
Overall Precision: 0.7181
Overall Recall: 0.7888
Overall F1: 0.7518
Overall Accuracy: 0.8109

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 OptimizerNames.ADAMW_TORCH_FUSED 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	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8118	1.0	10	1.6165	{'precision': 0.0053475935828877, 'recall': 0.003708281829419036, 'f1': 0.004379562043795621, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.1696969696969697, 'recall': 0.07887323943661972, 'f1': 0.10769230769230771, 'number': 1065}	0.0824	0.0437	0.0571	0.3296
1.4873	2.0	20	1.2837	{'precision': 0.2286302780638517, 'recall': 0.27441285537700866, 'f1': 0.24943820224719102, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.40404040404040403, 'recall': 0.48826291079812206, 'f1': 0.4421768707482993, 'number': 1065}	0.3286	0.3723	0.3491	0.5988
1.1439	3.0	30	0.9604	{'precision': 0.4777777777777778, 'recall': 0.5315203955500618, 'f1': 0.5032182562902282, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5338345864661654, 'recall': 0.6666666666666666, 'f1': 0.5929018789144049, 'number': 1065}	0.5096	0.5720	0.5390	0.7009
0.8769	4.0	40	0.8082	{'precision': 0.5680473372781065, 'recall': 0.7119901112484549, 'f1': 0.6319253976961053, 'number': 809}	{'precision': 0.17142857142857143, 'recall': 0.05042016806722689, 'f1': 0.07792207792207793, 'number': 119}	{'precision': 0.634046052631579, 'recall': 0.723943661971831, 'f1': 0.676019289785182, 'number': 1065}	0.5974	0.6789	0.6355	0.7460
0.7067	5.0	50	0.7335	{'precision': 0.6294363256784968, 'recall': 0.7453646477132262, 'f1': 0.6825127334465195, 'number': 809}	{'precision': 0.1875, 'recall': 0.12605042016806722, 'f1': 0.1507537688442211, 'number': 119}	{'precision': 0.6707818930041153, 'recall': 0.7652582159624414, 'f1': 0.7149122807017545, 'number': 1065}	0.6360	0.7190	0.6750	0.7739
0.6007	6.0	60	0.7113	{'precision': 0.6472361809045226, 'recall': 0.796044499381953, 'f1': 0.7139689578713968, 'number': 809}	{'precision': 0.18604651162790697, 'recall': 0.13445378151260504, 'f1': 0.15609756097560976, 'number': 119}	{'precision': 0.7309377738825592, 'recall': 0.7830985915492957, 'f1': 0.7561196736174071, 'number': 1065}	0.6724	0.7496	0.7089	0.7808
0.5219	7.0	70	0.6749	{'precision': 0.6618257261410788, 'recall': 0.788627935723115, 'f1': 0.7196841511562324, 'number': 809}	{'precision': 0.2066115702479339, 'recall': 0.21008403361344538, 'f1': 0.20833333333333334, 'number': 119}	{'precision': 0.726649528706084, 'recall': 0.7962441314553991, 'f1': 0.7598566308243728, 'number': 1065}	0.6710	0.7582	0.7119	0.7955
0.4628	8.0	80	0.6588	{'precision': 0.6902465166130761, 'recall': 0.796044499381953, 'f1': 0.7393800229621125, 'number': 809}	{'precision': 0.2608695652173913, 'recall': 0.25210084033613445, 'f1': 0.2564102564102564, 'number': 119}	{'precision': 0.7422852376980817, 'recall': 0.8356807511737089, 'f1': 0.7862190812720848, 'number': 1065}	0.6960	0.7847	0.7377	0.8061
0.4095	9.0	90	0.6676	{'precision': 0.6787941787941788, 'recall': 0.8071693448702101, 'f1': 0.7374364765669114, 'number': 809}	{'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119}	{'precision': 0.769163763066202, 'recall': 0.8291079812206573, 'f1': 0.7980117487573429, 'number': 1065}	0.7066	0.7868	0.7445	0.8060
0.3940	10.0	100	0.6780	{'precision': 0.7039473684210527, 'recall': 0.7935723114956736, 'f1': 0.7460778617083091, 'number': 809}	{'precision': 0.288135593220339, 'recall': 0.2857142857142857, 'f1': 0.2869198312236287, 'number': 119}	{'precision': 0.7744165946413137, 'recall': 0.8413145539906103, 'f1': 0.8064806480648066, 'number': 1065}	0.7188	0.7888	0.7522	0.8091
0.3427	11.0	110	0.6791	{'precision': 0.7005347593582888, 'recall': 0.8096415327564895, 'f1': 0.7511467889908257, 'number': 809}	{'precision': 0.24025974025974026, 'recall': 0.31092436974789917, 'f1': 0.2710622710622711, 'number': 119}	{'precision': 0.7715780296425457, 'recall': 0.8309859154929577, 'f1': 0.8001808318264014, 'number': 1065}	0.7053	0.7913	0.7458	0.8075
0.3233	12.0	120	0.6765	{'precision': 0.6941176470588235, 'recall': 0.8022249690976514, 'f1': 0.7442660550458714, 'number': 809}	{'precision': 0.27049180327868855, 'recall': 0.2773109243697479, 'f1': 0.27385892116182575, 'number': 119}	{'precision': 0.7831111111111111, 'recall': 0.8272300469483568, 'f1': 0.8045662100456622, 'number': 1065}	0.7163	0.7842	0.7487	0.8096
0.3056	13.0	130	0.6867	{'precision': 0.6944745395449621, 'recall': 0.792336217552534, 'f1': 0.7401847575057737, 'number': 809}	{'precision': 0.2702702702702703, 'recall': 0.33613445378151263, 'f1': 0.299625468164794, 'number': 119}	{'precision': 0.7764192139737991, 'recall': 0.8347417840375587, 'f1': 0.8045248868778281, 'number': 1065}	0.7085	0.7878	0.7460	0.8108
0.2898	14.0	140	0.6837	{'precision': 0.6989130434782609, 'recall': 0.7948084054388134, 'f1': 0.7437825332562175, 'number': 809}	{'precision': 0.2887323943661972, 'recall': 0.3445378151260504, 'f1': 0.31417624521072796, 'number': 119}	{'precision': 0.7856510186005314, 'recall': 0.8328638497652582, 'f1': 0.8085688240656335, 'number': 1065}	0.7170	0.7883	0.7510	0.8110
0.2827	15.0	150	0.6849	{'precision': 0.7012987012987013, 'recall': 0.8009888751545118, 'f1': 0.7478361223312175, 'number': 809}	{'precision': 0.2949640287769784, 'recall': 0.3445378151260504, 'f1': 0.31782945736434104, 'number': 119}	{'precision': 0.7841918294849023, 'recall': 0.8291079812206573, 'f1': 0.8060246462802373, 'number': 1065}	0.7181	0.7888	0.7518	0.8109

Framework versions

Transformers 5.7.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

billyprodev/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

billyprodev/
layoutlm-funsd