Loss: 0.6975
Answer: {'precision': 0.7057522123893806, 'recall': 0.788627935723115, 'f1': 0.7448920023350847, 'number': 809}
Header: {'precision': 0.2748091603053435, 'recall': 0.3025210084033613, 'f1': 0.288, 'number': 119}
Question: {'precision': 0.7804232804232805, 'recall': 0.8309859154929577, 'f1': 0.8049113233287858, 'number': 1065}
Overall Precision: 0.7188
Overall Recall: 0.7822
Overall F1: 0.7492
Overall Accuracy: 0.8031

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	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8422	1.0	10	1.6206	{'precision': 0.025280898876404494, 'recall': 0.022249690976514216, 'f1': 0.023668639053254437, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2301255230125523, 'recall': 0.15492957746478872, 'f1': 0.18518518518518517, 'number': 1065}	0.1277	0.0918	0.1068	0.3520
1.4509	2.0	20	1.2639	{'precision': 0.15125, 'recall': 0.14956736711990112, 'f1': 0.15040397762585456, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.465642683912692, 'recall': 0.5408450704225352, 'f1': 0.5004344048653345, 'number': 1065}	0.3420	0.3497	0.3458	0.5726
1.1141	3.0	30	0.9554	{'precision': 0.5079872204472844, 'recall': 0.5896168108776267, 'f1': 0.5457665903890161, 'number': 809}	{'precision': 0.07894736842105263, 'recall': 0.025210084033613446, 'f1': 0.038216560509554146, 'number': 119}	{'precision': 0.6070007955449482, 'recall': 0.7164319248826291, 'f1': 0.6571920757967269, 'number': 1065}	0.5564	0.6237	0.5881	0.7246
0.8541	4.0	40	0.7774	{'precision': 0.5957446808510638, 'recall': 0.69221260815822, 'f1': 0.6403659233847913, 'number': 809}	{'precision': 0.19117647058823528, 'recall': 0.1092436974789916, 'f1': 0.13903743315508021, 'number': 119}	{'precision': 0.6491228070175439, 'recall': 0.7643192488262911, 'f1': 0.7020267356619233, 'number': 1065}	0.6132	0.6959	0.6519	0.7636
0.6793	5.0	50	0.7244	{'precision': 0.6372549019607843, 'recall': 0.723114956736712, 'f1': 0.677475390851187, 'number': 809}	{'precision': 0.23076923076923078, 'recall': 0.15126050420168066, 'f1': 0.18274111675126906, 'number': 119}	{'precision': 0.6705516705516705, 'recall': 0.8103286384976526, 'f1': 0.733843537414966, 'number': 1065}	0.6421	0.7356	0.6857	0.7706
0.5888	6.0	60	0.6842	{'precision': 0.6595517609391676, 'recall': 0.7639060568603214, 'f1': 0.7079037800687286, 'number': 809}	{'precision': 0.2597402597402597, 'recall': 0.16806722689075632, 'f1': 0.20408163265306123, 'number': 119}	{'precision': 0.7196339434276207, 'recall': 0.812206572769953, 'f1': 0.7631230701367445, 'number': 1065}	0.6782	0.7541	0.7142	0.7847
0.5107	7.0	70	0.6648	{'precision': 0.6694473409801877, 'recall': 0.7935723114956736, 'f1': 0.7262443438914026, 'number': 809}	{'precision': 0.24561403508771928, 'recall': 0.23529411764705882, 'f1': 0.24034334763948498, 'number': 119}	{'precision': 0.7504347826086957, 'recall': 0.8103286384976526, 'f1': 0.779232505643341, 'number': 1065}	0.6896	0.7692	0.7272	0.7944
0.455	8.0	80	0.6582	{'precision': 0.6750788643533123, 'recall': 0.7935723114956736, 'f1': 0.7295454545454545, 'number': 809}	{'precision': 0.25961538461538464, 'recall': 0.226890756302521, 'f1': 0.242152466367713, 'number': 119}	{'precision': 0.75, 'recall': 0.8253521126760563, 'f1': 0.7858739383102369, 'number': 1065}	0.6951	0.7767	0.7336	0.7990
0.4012	9.0	90	0.6598	{'precision': 0.6955093099671413, 'recall': 0.7849196538936959, 'f1': 0.7375145180023228, 'number': 809}	{'precision': 0.25203252032520324, 'recall': 0.2605042016806723, 'f1': 0.25619834710743805, 'number': 119}	{'precision': 0.7566409597257926, 'recall': 0.8291079812206573, 'f1': 0.7912186379928315, 'number': 1065}	0.7031	0.7772	0.7383	0.7989
0.3934	10.0	100	0.6706	{'precision': 0.7069716775599129, 'recall': 0.8022249690976514, 'f1': 0.751592356687898, 'number': 809}	{'precision': 0.27586206896551724, 'recall': 0.2689075630252101, 'f1': 0.27234042553191484, 'number': 119}	{'precision': 0.7775784753363228, 'recall': 0.8140845070422535, 'f1': 0.7954128440366972, 'number': 1065}	0.7203	0.7767	0.7475	0.8052
0.3354	11.0	110	0.6780	{'precision': 0.7046688382193268, 'recall': 0.8022249690976514, 'f1': 0.7502890173410405, 'number': 809}	{'precision': 0.272, 'recall': 0.2857142857142857, 'f1': 0.27868852459016397, 'number': 119}	{'precision': 0.7675628794449263, 'recall': 0.8309859154929577, 'f1': 0.7980162308385933, 'number': 1065}	0.7131	0.7868	0.7481	0.8028
0.3187	12.0	120	0.6842	{'precision': 0.7097130242825607, 'recall': 0.7948084054388134, 'f1': 0.7498542274052479, 'number': 809}	{'precision': 0.26717557251908397, 'recall': 0.29411764705882354, 'f1': 0.28, 'number': 119}	{'precision': 0.7742504409171076, 'recall': 0.8244131455399061, 'f1': 0.7985447930877672, 'number': 1065}	0.7167	0.7807	0.7474	0.8021
0.3007	13.0	130	0.6944	{'precision': 0.704225352112676, 'recall': 0.8034610630407911, 'f1': 0.7505773672055426, 'number': 809}	{'precision': 0.2833333333333333, 'recall': 0.2857142857142857, 'f1': 0.2845188284518828, 'number': 119}	{'precision': 0.7791519434628975, 'recall': 0.828169014084507, 'f1': 0.8029130632680928, 'number': 1065}	0.72	0.7858	0.7514	0.8022
0.2805	14.0	140	0.7007	{'precision': 0.7126948775055679, 'recall': 0.7911001236093943, 'f1': 0.7498535442296427, 'number': 809}	{'precision': 0.2695035460992908, 'recall': 0.31932773109243695, 'f1': 0.2923076923076923, 'number': 119}	{'precision': 0.7805530776092774, 'recall': 0.8215962441314554, 'f1': 0.8005489478499542, 'number': 1065}	0.7190	0.7792	0.7479	0.8010
0.2795	15.0	150	0.6975	{'precision': 0.7057522123893806, 'recall': 0.788627935723115, 'f1': 0.7448920023350847, 'number': 809}	{'precision': 0.2748091603053435, 'recall': 0.3025210084033613, 'f1': 0.288, 'number': 119}	{'precision': 0.7804232804232805, 'recall': 0.8309859154929577, 'f1': 0.8049113233287858, 'number': 1065}	0.7188	0.7822	0.7492	0.8031

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.20.3

shivaramnerella/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

shivaramnerella/
layoutlm-funsd