Loss: 0.7204
Answer: {'precision': 0.7103218645948945, 'recall': 0.7911001236093943, 'f1': 0.7485380116959064, 'number': 809}
Header: {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}
Question: {'precision': 0.7799126637554585, 'recall': 0.8384976525821596, 'f1': 0.8081447963800905, 'number': 1065}
Overall Precision: 0.7284
Overall Recall: 0.7913
Overall F1: 0.7585
Overall Accuracy: 0.7930

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

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8258	1.0	10	1.6104	{'precision': 0.03933136676499508, 'recall': 0.049443757725587144, 'f1': 0.04381161007667032, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2102076124567474, 'recall': 0.22816901408450704, 'f1': 0.21882035119315627, 'number': 1065}	0.1302	0.1420	0.1359	0.3742
1.4584	2.0	20	1.2730	{'precision': 0.1923509561304837, 'recall': 0.21137206427688504, 'f1': 0.20141342756183747, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4099290780141844, 'recall': 0.5427230046948357, 'f1': 0.467070707070707, 'number': 1065}	0.3258	0.3758	0.3490	0.5734
1.1076	3.0	30	0.9718	{'precision': 0.501532175689479, 'recall': 0.6069221260815822, 'f1': 0.5492170022371365, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5749211356466877, 'recall': 0.6845070422535211, 'f1': 0.6249464209172738, 'number': 1065}	0.5358	0.6121	0.5714	0.6890
0.8541	4.0	40	0.8287	{'precision': 0.5573921028466483, 'recall': 0.7503090234857849, 'f1': 0.6396206533192834, 'number': 809}	{'precision': 0.14893617021276595, 'recall': 0.058823529411764705, 'f1': 0.08433734939759036, 'number': 119}	{'precision': 0.6451342281879194, 'recall': 0.7220657276995305, 'f1': 0.6814355338945502, 'number': 1065}	0.5941	0.6939	0.6401	0.7420
0.7105	5.0	50	0.7483	{'precision': 0.6358695652173914, 'recall': 0.723114956736712, 'f1': 0.6766917293233082, 'number': 809}	{'precision': 0.2465753424657534, 'recall': 0.15126050420168066, 'f1': 0.18749999999999997, 'number': 119}	{'precision': 0.6898305084745763, 'recall': 0.7643192488262911, 'f1': 0.7251670378619154, 'number': 1065}	0.6521	0.7110	0.6803	0.7618
0.6079	6.0	60	0.7023	{'precision': 0.6306209850107066, 'recall': 0.7280593325092707, 'f1': 0.6758462421113023, 'number': 809}	{'precision': 0.2875, 'recall': 0.19327731092436976, 'f1': 0.23115577889447236, 'number': 119}	{'precision': 0.6796267496111975, 'recall': 0.8206572769953052, 'f1': 0.7435133985538068, 'number': 1065}	0.6461	0.7456	0.6923	0.7776
0.5267	7.0	70	0.6779	{'precision': 0.674892703862661, 'recall': 0.7775030902348579, 'f1': 0.7225732337736933, 'number': 809}	{'precision': 0.3, 'recall': 0.226890756302521, 'f1': 0.25837320574162675, 'number': 119}	{'precision': 0.717391304347826, 'recall': 0.8056338028169014, 'f1': 0.7589562140645733, 'number': 1065}	0.6826	0.7597	0.7191	0.7853
0.4735	8.0	80	0.6688	{'precision': 0.6955093099671413, 'recall': 0.7849196538936959, 'f1': 0.7375145180023228, 'number': 809}	{'precision': 0.32608695652173914, 'recall': 0.25210084033613445, 'f1': 0.2843601895734597, 'number': 119}	{'precision': 0.7424892703862661, 'recall': 0.812206572769953, 'f1': 0.7757847533632287, 'number': 1065}	0.7051	0.7677	0.7350	0.7950
0.4196	9.0	90	0.6791	{'precision': 0.6843243243243243, 'recall': 0.7824474660074165, 'f1': 0.7301038062283737, 'number': 809}	{'precision': 0.3181818181818182, 'recall': 0.29411764705882354, 'f1': 0.3056768558951965, 'number': 119}	{'precision': 0.7561807331628303, 'recall': 0.8328638497652582, 'f1': 0.7926720285969615, 'number': 1065}	0.7043	0.7802	0.7403	0.7937
0.3756	10.0	100	0.6968	{'precision': 0.7089887640449438, 'recall': 0.7799752781211372, 'f1': 0.7427898763978811, 'number': 809}	{'precision': 0.328, 'recall': 0.3445378151260504, 'f1': 0.33606557377049184, 'number': 119}	{'precision': 0.7790393013100436, 'recall': 0.8375586854460094, 'f1': 0.807239819004525, 'number': 1065}	0.7241	0.7847	0.7532	0.7947
0.3402	11.0	110	0.6959	{'precision': 0.7024070021881839, 'recall': 0.7935723114956736, 'f1': 0.7452118398142775, 'number': 809}	{'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}	{'precision': 0.7791411042944786, 'recall': 0.8347417840375587, 'f1': 0.8059836808703537, 'number': 1065}	0.7228	0.7888	0.7543	0.7958
0.3225	12.0	120	0.6945	{'precision': 0.7106430155210643, 'recall': 0.792336217552534, 'f1': 0.7492694330800702, 'number': 809}	{'precision': 0.3644067796610169, 'recall': 0.36134453781512604, 'f1': 0.3628691983122363, 'number': 119}	{'precision': 0.7667238421955404, 'recall': 0.8394366197183099, 'f1': 0.8014343343792021, 'number': 1065}	0.7219	0.7918	0.7552	0.7961
0.3031	13.0	130	0.7204	{'precision': 0.71, 'recall': 0.7898640296662547, 'f1': 0.7478057343475717, 'number': 809}	{'precision': 0.35, 'recall': 0.35294117647058826, 'f1': 0.35146443514644354, 'number': 119}	{'precision': 0.7895204262877442, 'recall': 0.8347417840375587, 'f1': 0.8115015974440895, 'number': 1065}	0.7316	0.7878	0.7586	0.7916
0.289	14.0	140	0.7196	{'precision': 0.7095709570957096, 'recall': 0.7972805933250927, 'f1': 0.750873108265425, 'number': 809}	{'precision': 0.3826086956521739, 'recall': 0.3697478991596639, 'f1': 0.37606837606837606, 'number': 119}	{'precision': 0.7816593886462883, 'recall': 0.8403755868544601, 'f1': 0.8099547511312217, 'number': 1065}	0.7303	0.7948	0.7612	0.7949
0.2801	15.0	150	0.7204	{'precision': 0.7103218645948945, 'recall': 0.7911001236093943, 'f1': 0.7485380116959064, 'number': 809}	{'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}	{'precision': 0.7799126637554585, 'recall': 0.8384976525821596, 'f1': 0.8081447963800905, 'number': 1065}	0.7284	0.7913	0.7585	0.7930

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1

sreejith8100/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sreejith8100/
layoutlm-funsd