Loss: 0.0018
Eader: {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}
Nswer: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}
Uestion: {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}
Overall Precision: 0.9955
Overall Recall: 0.9978
Overall F1: 0.9967
Overall Accuracy: 0.9998

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
0.0276	1.0	8	0.0029	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 0.9943181818181818, 'recall': 0.9943181818181818, 'f1': 0.9943181818181818, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9933	0.9955	0.9944	0.9995
0.0298	2.0	16	0.0053	{'precision': 0.9558823529411765, 'recall': 0.9701492537313433, 'f1': 0.962962962962963, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9806763285024155, 'recall': 0.9902439024390244, 'f1': 0.9854368932038836, 'number': 205}	0.9845	0.9911	0.9878	0.9993
0.0256	3.0	24	0.0027	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0177	4.0	32	0.0024	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0129	5.0	40	0.0022	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.012	6.0	48	0.0021	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0092	7.0	56	0.0019	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0079	8.0	64	0.0018	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0075	9.0	72	0.0018	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0078	10.0	80	0.0019	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0073	11.0	88	0.0020	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0063	12.0	96	0.0021	{'precision': 0.9705882352941176, 'recall': 0.9850746268656716, 'f1': 0.9777777777777777, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9855072463768116, 'recall': 0.9951219512195122, 'f1': 0.9902912621359223, 'number': 205}	0.9889	0.9955	0.9922	0.9995
0.0054	13.0	104	0.0018	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0046	14.0	112	0.0018	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998
0.0052	15.0	120	0.0018	{'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}	{'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}	0.9955	0.9978	0.9967	0.9998

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

pabloma09/layoutlm-FUNSD-only-5fold

layoutlm-FUNSD-only-5fold

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

pabloma09/
layoutlm-FUNSD-only-5fold