Loss: 0.7960
Answer: {'precision': 0.7169603524229075, 'recall': 0.8046971569839307, 'f1': 0.7582993593476993, 'number': 809}
Header: {'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119}
Question: {'precision': 0.7883408071748879, 'recall': 0.8253521126760563, 'f1': 0.8064220183486238, 'number': 1065}
Overall Precision: 0.7307
Overall Recall: 0.7938
Overall F1: 0.7609
Overall Accuracy: 0.8081

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: 6
eval_batch_size: 4
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
1.6386	1.0	25	1.2949	{'precision': 0.08352668213457076, 'recall': 0.08899876390605686, 'f1': 0.08617594254937162, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.36874571624400276, 'recall': 0.5051643192488263, 'f1': 0.42630744849445323, 'number': 1065}	0.2628	0.3061	0.2828	0.5116
1.0433	2.0	50	0.8005	{'precision': 0.5965447154471545, 'recall': 0.7255871446229913, 'f1': 0.6547685443390964, 'number': 809}	{'precision': 0.1111111111111111, 'recall': 0.058823529411764705, 'f1': 0.07692307692307691, 'number': 119}	{'precision': 0.6574487065120428, 'recall': 0.692018779342723, 'f1': 0.6742909423604757, 'number': 1065}	0.6139	0.6678	0.6398	0.7293
0.6891	3.0	75	0.6695	{'precision': 0.6335650446871897, 'recall': 0.788627935723115, 'f1': 0.7026431718061674, 'number': 809}	{'precision': 0.3246753246753247, 'recall': 0.21008403361344538, 'f1': 0.25510204081632654, 'number': 119}	{'precision': 0.7085862966175195, 'recall': 0.7671361502347418, 'f1': 0.7366997294860236, 'number': 1065}	0.6616	0.7426	0.6998	0.7752
0.532	4.0	100	0.6270	{'precision': 0.6573787409700722, 'recall': 0.7873918417799752, 'f1': 0.7165354330708661, 'number': 809}	{'precision': 0.2361111111111111, 'recall': 0.2857142857142857, 'f1': 0.25855513307984795, 'number': 119}	{'precision': 0.7153284671532847, 'recall': 0.828169014084507, 'f1': 0.7676240208877285, 'number': 1065}	0.6620	0.7792	0.7158	0.7961
0.4184	5.0	125	0.6174	{'precision': 0.6837160751565762, 'recall': 0.8096415327564895, 'f1': 0.7413695529145445, 'number': 809}	{'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}	{'precision': 0.7734657039711191, 'recall': 0.8046948356807512, 'f1': 0.7887712839392544, 'number': 1065}	0.7102	0.7757	0.7415	0.8025
0.3264	6.0	150	0.6493	{'precision': 0.6905537459283387, 'recall': 0.7861557478368356, 'f1': 0.7352601156069365, 'number': 809}	{'precision': 0.310126582278481, 'recall': 0.4117647058823529, 'f1': 0.35379061371841153, 'number': 119}	{'precision': 0.7713523131672598, 'recall': 0.8140845070422535, 'f1': 0.7921425308359983, 'number': 1065}	0.7045	0.7787	0.7398	0.8008
0.2661	7.0	175	0.6587	{'precision': 0.6857440166493236, 'recall': 0.8145859085290482, 'f1': 0.7446327683615819, 'number': 809}	{'precision': 0.32575757575757575, 'recall': 0.36134453781512604, 'f1': 0.3426294820717131, 'number': 119}	{'precision': 0.7720970537261699, 'recall': 0.8366197183098592, 'f1': 0.8030644434429923, 'number': 1065}	0.7089	0.7993	0.7514	0.8038
0.2246	8.0	200	0.7115	{'precision': 0.7111356119073869, 'recall': 0.7972805933250927, 'f1': 0.7517482517482517, 'number': 809}	{'precision': 0.2983425414364641, 'recall': 0.453781512605042, 'f1': 0.36, 'number': 119}	{'precision': 0.7891402714932126, 'recall': 0.8187793427230047, 'f1': 0.8036866359447005, 'number': 1065}	0.7164	0.7883	0.7506	0.8074
0.1928	9.0	225	0.7130	{'precision': 0.7094668117519043, 'recall': 0.8059332509270705, 'f1': 0.7546296296296295, 'number': 809}	{'precision': 0.3178294573643411, 'recall': 0.3445378151260504, 'f1': 0.33064516129032256, 'number': 119}	{'precision': 0.7908025247971145, 'recall': 0.8234741784037559, 'f1': 0.8068077276908925, 'number': 1065}	0.7279	0.7878	0.7566	0.8042
0.1598	10.0	250	0.7375	{'precision': 0.7242937853107345, 'recall': 0.792336217552534, 'f1': 0.756788665879575, 'number': 809}	{'precision': 0.375, 'recall': 0.42857142857142855, 'f1': 0.39999999999999997, 'number': 119}	{'precision': 0.788858939802336, 'recall': 0.8244131455399061, 'f1': 0.8062442607897153, 'number': 1065}	0.7357	0.7878	0.7608	0.8099
0.1444	11.0	275	0.7719	{'precision': 0.7027896995708155, 'recall': 0.8096415327564895, 'f1': 0.7524411257897761, 'number': 809}	{'precision': 0.34814814814814815, 'recall': 0.3949579831932773, 'f1': 0.3700787401574803, 'number': 119}	{'precision': 0.7825311942959001, 'recall': 0.8244131455399061, 'f1': 0.8029263831732967, 'number': 1065}	0.7218	0.7928	0.7556	0.8008
0.1251	12.0	300	0.7758	{'precision': 0.7133479212253829, 'recall': 0.8059332509270705, 'f1': 0.7568195008705745, 'number': 809}	{'precision': 0.38095238095238093, 'recall': 0.40336134453781514, 'f1': 0.39183673469387753, 'number': 119}	{'precision': 0.7880434782608695, 'recall': 0.8169014084507042, 'f1': 0.8022130013831259, 'number': 1065}	0.7323	0.7878	0.7590	0.8077
0.1124	13.0	325	0.7878	{'precision': 0.7150776053215078, 'recall': 0.7972805933250927, 'f1': 0.7539450613676213, 'number': 809}	{'precision': 0.38848920863309355, 'recall': 0.453781512605042, 'f1': 0.4186046511627907, 'number': 119}	{'precision': 0.7922312556458898, 'recall': 0.8234741784037559, 'f1': 0.8075506445672191, 'number': 1065}	0.7337	0.7908	0.7612	0.8094
0.1077	14.0	350	0.7945	{'precision': 0.7136612021857923, 'recall': 0.8071693448702101, 'f1': 0.7575406032482598, 'number': 809}	{'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119}	{'precision': 0.7887197851387645, 'recall': 0.8272300469483568, 'f1': 0.8075160403299725, 'number': 1065}	0.7295	0.7958	0.7612	0.8098
0.1001	15.0	375	0.7960	{'precision': 0.7169603524229075, 'recall': 0.8046971569839307, 'f1': 0.7582993593476993, 'number': 809}	{'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119}	{'precision': 0.7883408071748879, 'recall': 0.8253521126760563, 'f1': 0.8064220183486238, 'number': 1065}	0.7307	0.7938	0.7609	0.8081

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3

alexneakameni/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alexneakameni/
layoutlm-funsd