Loss: 0.6766
Answer: {'precision': 0.6937033084311632, 'recall': 0.8034610630407911, 'f1': 0.7445589919816725, 'number': 809}
Header: {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119}
Question: {'precision': 0.7879858657243817, 'recall': 0.8375586854460094, 'f1': 0.812016385980883, 'number': 1065}
Overall Precision: 0.7226
Overall Recall: 0.7948
Overall F1: 0.7570
Overall Accuracy: 0.8076

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
1.7798	1.0	10	1.5839	{'precision': 0.01451378809869376, 'recall': 0.012360939431396786, 'f1': 0.01335113484646195, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.1924342105263158, 'recall': 0.10985915492957747, 'f1': 0.1398684997011357, 'number': 1065}	0.0979	0.0637	0.0772	0.3437
1.4432	2.0	20	1.2365	{'precision': 0.24876604146100692, 'recall': 0.311495673671199, 'f1': 0.27661909989023054, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4279411764705882, 'recall': 0.5464788732394367, 'f1': 0.48000000000000004, 'number': 1065}	0.3515	0.4185	0.3820	0.5956
1.0756	3.0	30	0.9004	{'precision': 0.5348399246704332, 'recall': 0.7021013597033374, 'f1': 0.6071619454836986, 'number': 809}	{'precision': 0.03225806451612903, 'recall': 0.008403361344537815, 'f1': 0.013333333333333332, 'number': 119}	{'precision': 0.5821917808219178, 'recall': 0.7183098591549296, 'f1': 0.6431273644388399, 'number': 1065}	0.5542	0.6693	0.6064	0.7160
0.8288	4.0	40	0.7621	{'precision': 0.6007984031936128, 'recall': 0.7441285537700866, 'f1': 0.6648260629486471, 'number': 809}	{'precision': 0.10526315789473684, 'recall': 0.06722689075630252, 'f1': 0.08205128205128205, 'number': 119}	{'precision': 0.6632825719120136, 'recall': 0.7361502347417841, 'f1': 0.6978193146417446, 'number': 1065}	0.6168	0.6994	0.6555	0.7647
0.6673	5.0	50	0.7300	{'precision': 0.6344314558979809, 'recall': 0.7379480840543882, 'f1': 0.6822857142857143, 'number': 809}	{'precision': 0.2247191011235955, 'recall': 0.16806722689075632, 'f1': 0.19230769230769232, 'number': 119}	{'precision': 0.6439909297052154, 'recall': 0.8, 'f1': 0.71356783919598, 'number': 1065}	0.6243	0.7371	0.6760	0.7704
0.5732	6.0	60	0.6729	{'precision': 0.6424974823766365, 'recall': 0.788627935723115, 'f1': 0.7081021087680356, 'number': 809}	{'precision': 0.21, 'recall': 0.17647058823529413, 'f1': 0.19178082191780824, 'number': 119}	{'precision': 0.7048494983277592, 'recall': 0.7915492957746478, 'f1': 0.7456877487837241, 'number': 1065}	0.6562	0.7536	0.7015	0.7928
0.5005	7.0	70	0.6515	{'precision': 0.6611740473738414, 'recall': 0.7935723114956736, 'f1': 0.7213483146067416, 'number': 809}	{'precision': 0.2288135593220339, 'recall': 0.226890756302521, 'f1': 0.22784810126582278, 'number': 119}	{'precision': 0.7432784041630529, 'recall': 0.8046948356807512, 'f1': 0.7727682596934174, 'number': 1065}	0.6806	0.7657	0.7207	0.8005
0.4473	8.0	80	0.6629	{'precision': 0.6748717948717948, 'recall': 0.8133498145859085, 'f1': 0.7376681614349776, 'number': 809}	{'precision': 0.27586206896551724, 'recall': 0.2689075630252101, 'f1': 0.27234042553191484, 'number': 119}	{'precision': 0.7614520311149524, 'recall': 0.8272300469483568, 'f1': 0.7929792979297929, 'number': 1065}	0.6988	0.7883	0.7409	0.7983
0.3953	9.0	90	0.6507	{'precision': 0.6750524109014675, 'recall': 0.796044499381953, 'f1': 0.7305728871242201, 'number': 809}	{'precision': 0.3008849557522124, 'recall': 0.2857142857142857, 'f1': 0.29310344827586204, 'number': 119}	{'precision': 0.7689625108979947, 'recall': 0.828169014084507, 'f1': 0.7974683544303798, 'number': 1065}	0.7046	0.7827	0.7416	0.8031
0.3926	10.0	100	0.6539	{'precision': 0.6704663212435233, 'recall': 0.799752781211372, 'f1': 0.729425028184893, 'number': 809}	{'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119}	{'precision': 0.7722513089005235, 'recall': 0.8309859154929577, 'f1': 0.8005427408412483, 'number': 1065}	0.7049	0.7873	0.7438	0.8046
0.3344	11.0	110	0.6626	{'precision': 0.6831578947368421, 'recall': 0.8022249690976514, 'f1': 0.7379192723138146, 'number': 809}	{'precision': 0.3140495867768595, 'recall': 0.31932773109243695, 'f1': 0.31666666666666665, 'number': 119}	{'precision': 0.7677029360967185, 'recall': 0.8347417840375587, 'f1': 0.7998200629779576, 'number': 1065}	0.7070	0.7908	0.7466	0.8054
0.316	12.0	120	0.6678	{'precision': 0.6871035940803383, 'recall': 0.8034610630407911, 'f1': 0.7407407407407406, 'number': 809}	{'precision': 0.31666666666666665, 'recall': 0.31932773109243695, 'f1': 0.3179916317991632, 'number': 119}	{'precision': 0.7860300618921309, 'recall': 0.8347417840375587, 'f1': 0.8096539162112932, 'number': 1065}	0.7178	0.7913	0.7527	0.8071
0.3001	13.0	130	0.6758	{'precision': 0.6856540084388185, 'recall': 0.8034610630407911, 'f1': 0.7398975526465565, 'number': 809}	{'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119}	{'precision': 0.792, 'recall': 0.8366197183098592, 'f1': 0.8136986301369863, 'number': 1065}	0.7205	0.7928	0.7549	0.8082
0.279	14.0	140	0.6750	{'precision': 0.6875, 'recall': 0.8022249690976514, 'f1': 0.7404449515116942, 'number': 809}	{'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119}	{'precision': 0.7816901408450704, 'recall': 0.8338028169014085, 'f1': 0.8069059518400727, 'number': 1065}	0.7151	0.7908	0.7510	0.8066
0.2793	15.0	150	0.6766	{'precision': 0.6937033084311632, 'recall': 0.8034610630407911, 'f1': 0.7445589919816725, 'number': 809}	{'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119}	{'precision': 0.7879858657243817, 'recall': 0.8375586854460094, 'f1': 0.812016385980883, 'number': 1065}	0.7226	0.7948	0.7570	0.8076

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

lmurray/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lmurray/
layoutlm-funsd