Loss: 0.6788
Answer: {'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809}
Header: {'precision': 0.3014705882352941, 'recall': 0.3445378151260504, 'f1': 0.3215686274509804, 'number': 119}
Question: {'precision': 0.7795414462081128, 'recall': 0.8300469483568075, 'f1': 0.8040018190086403, 'number': 1065}
Overall Precision: 0.7185
Overall Recall: 0.7928
Overall F1: 0.7538
Overall Accuracy: 0.8119

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
1.8004	1.0	10	1.6077	{'precision': 0.013595166163141994, 'recall': 0.011124845488257108, 'f1': 0.012236573759347382, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.24475524475524477, 'recall': 0.13145539906103287, 'f1': 0.1710445937690898, 'number': 1065}	0.1207	0.0748	0.0923	0.3510
1.4746	2.0	20	1.2888	{'precision': 0.1705521472392638, 'recall': 0.17181705809641531, 'f1': 0.1711822660098522, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4061776061776062, 'recall': 0.49389671361502346, 'f1': 0.4457627118644068, 'number': 1065}	0.3152	0.3337	0.3242	0.5672
1.1291	3.0	30	0.9811	{'precision': 0.5127931769722814, 'recall': 0.5945611866501854, 'f1': 0.5506582713222669, 'number': 809}	{'precision': 0.027777777777777776, 'recall': 0.008403361344537815, 'f1': 0.012903225806451613, 'number': 119}	{'precision': 0.5457317073170732, 'recall': 0.672300469483568, 'f1': 0.6024400504838031, 'number': 1065}	0.5241	0.6011	0.5599	0.7097
0.86	4.0	40	0.8044	{'precision': 0.5934489402697495, 'recall': 0.761433868974042, 'f1': 0.6670276123443422, 'number': 809}	{'precision': 0.18333333333333332, 'recall': 0.09243697478991597, 'f1': 0.12290502793296088, 'number': 119}	{'precision': 0.6554694229112834, 'recall': 0.7145539906103286, 'f1': 0.683737646001797, 'number': 1065}	0.6144	0.6964	0.6529	0.7534
0.6873	5.0	50	0.7263	{'precision': 0.6666666666666666, 'recall': 0.7416563658838071, 'f1': 0.7021650087770627, 'number': 809}	{'precision': 0.2777777777777778, 'recall': 0.21008403361344538, 'f1': 0.23923444976076552, 'number': 119}	{'precision': 0.6648648648648648, 'recall': 0.8084507042253521, 'f1': 0.7296610169491525, 'number': 1065}	0.6503	0.7456	0.6947	0.7838
0.5806	6.0	60	0.6815	{'precision': 0.6598569969356486, 'recall': 0.7985166872682324, 'f1': 0.7225950782997763, 'number': 809}	{'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119}	{'precision': 0.7074829931972789, 'recall': 0.7812206572769953, 'f1': 0.7425256581883088, 'number': 1065}	0.6681	0.7536	0.7083	0.7901
0.5036	7.0	70	0.6550	{'precision': 0.6694473409801877, 'recall': 0.7935723114956736, 'f1': 0.7262443438914026, 'number': 809}	{'precision': 0.23076923076923078, 'recall': 0.226890756302521, 'f1': 0.22881355932203387, 'number': 119}	{'precision': 0.7226962457337884, 'recall': 0.7953051643192488, 'f1': 0.7572641931157801, 'number': 1065}	0.6744	0.7607	0.7149	0.7975
0.4447	8.0	80	0.6628	{'precision': 0.67570385818561, 'recall': 0.8009888751545118, 'f1': 0.7330316742081447, 'number': 809}	{'precision': 0.24390243902439024, 'recall': 0.25210084033613445, 'f1': 0.24793388429752067, 'number': 119}	{'precision': 0.7413494809688581, 'recall': 0.8046948356807512, 'f1': 0.7717244484466456, 'number': 1065}	0.6859	0.7702	0.7256	0.7973
0.392	9.0	90	0.6465	{'precision': 0.6974248927038627, 'recall': 0.8034610630407911, 'f1': 0.7466973004020677, 'number': 809}	{'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119}	{'precision': 0.7433476394849785, 'recall': 0.8131455399061033, 'f1': 0.7766816143497759, 'number': 1065}	0.7001	0.7802	0.7380	0.8060
0.3844	10.0	100	0.6466	{'precision': 0.6900212314225053, 'recall': 0.8034610630407911, 'f1': 0.7424328954882924, 'number': 809}	{'precision': 0.28440366972477066, 'recall': 0.2605042016806723, 'f1': 0.2719298245614035, 'number': 119}	{'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065}	0.7114	0.7827	0.7453	0.8170
0.323	11.0	110	0.6688	{'precision': 0.7047930283224401, 'recall': 0.799752781211372, 'f1': 0.7492762015055008, 'number': 809}	{'precision': 0.2808219178082192, 'recall': 0.3445378151260504, 'f1': 0.309433962264151, 'number': 119}	{'precision': 0.7660869565217391, 'recall': 0.8272300469483568, 'f1': 0.7954853273137698, 'number': 1065}	0.7087	0.7873	0.7459	0.8081
0.3034	12.0	120	0.6660	{'precision': 0.7082429501084598, 'recall': 0.8071693448702101, 'f1': 0.754477180820335, 'number': 809}	{'precision': 0.3559322033898305, 'recall': 0.35294117647058826, 'f1': 0.35443037974683544, 'number': 119}	{'precision': 0.7871956717763751, 'recall': 0.819718309859155, 'f1': 0.8031278748850045, 'number': 1065}	0.7296	0.7868	0.7571	0.8138
0.2884	13.0	130	0.6788	{'precision': 0.7159956474428727, 'recall': 0.8133498145859085, 'f1': 0.7615740740740741, 'number': 809}	{'precision': 0.328125, 'recall': 0.35294117647058826, 'f1': 0.340080971659919, 'number': 119}	{'precision': 0.7803365810451727, 'recall': 0.8272300469483568, 'f1': 0.8030993618960802, 'number': 1065}	0.7266	0.7933	0.7585	0.8110
0.2674	14.0	140	0.6781	{'precision': 0.7114967462039046, 'recall': 0.8108776266996292, 'f1': 0.7579433853264009, 'number': 809}	{'precision': 0.30597014925373134, 'recall': 0.3445378151260504, 'f1': 0.3241106719367589, 'number': 119}	{'precision': 0.7848888888888889, 'recall': 0.8291079812206573, 'f1': 0.8063926940639269, 'number': 1065}	0.7244	0.7928	0.7571	0.8133
0.271	15.0	150	0.6788	{'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809}	{'precision': 0.3014705882352941, 'recall': 0.3445378151260504, 'f1': 0.3215686274509804, 'number': 119}	{'precision': 0.7795414462081128, 'recall': 0.8300469483568075, 'f1': 0.8040018190086403, 'number': 1065}	0.7185	0.7928	0.7538	0.8119

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

sachin18/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sachin18/
layoutlm-funsd