Loss: 0.6741
Answer: {'precision': 0.6960167714884696, 'recall': 0.8207663782447466, 'f1': 0.7532614861032332, 'number': 809}
Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
Question: {'precision': 0.7824529991047449, 'recall': 0.8206572769953052, 'f1': 0.8010999083409717, 'number': 1065}
Overall Precision: 0.7178
Overall Recall: 0.7913
Overall F1: 0.7527
Overall Accuracy: 0.8085

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
1.8231	1.0	10	1.5809	{'precision': 0.02072538860103627, 'recall': 0.024721878862793572, 'f1': 0.022547914317925594, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.20584795321637428, 'recall': 0.1652582159624413, 'f1': 0.18333333333333335, 'number': 1065}	0.1077	0.0983	0.1028	0.4047
1.4405	2.0	20	1.2298	{'precision': 0.12202380952380952, 'recall': 0.10135970333745364, 'f1': 0.11073598919648886, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4417808219178082, 'recall': 0.6056338028169014, 'f1': 0.5108910891089109, 'number': 1065}	0.3407	0.3648	0.3523	0.5730
1.111	3.0	30	0.9547	{'precision': 0.47729672650475186, 'recall': 0.5587144622991347, 'f1': 0.5148063781321185, 'number': 809}	{'precision': 0.06451612903225806, 'recall': 0.01680672268907563, 'f1': 0.026666666666666665, 'number': 119}	{'precision': 0.6218274111675127, 'recall': 0.6901408450704225, 'f1': 0.6542056074766356, 'number': 1065}	0.5505	0.5966	0.5726	0.7074
0.8317	4.0	40	0.7729	{'precision': 0.5933649289099526, 'recall': 0.7737948084054388, 'f1': 0.6716738197424893, 'number': 809}	{'precision': 0.24528301886792453, 'recall': 0.1092436974789916, 'f1': 0.1511627906976744, 'number': 119}	{'precision': 0.6753574432296047, 'recall': 0.7539906103286385, 'f1': 0.7125110913930789, 'number': 1065}	0.6278	0.7235	0.6723	0.7637
0.656	5.0	50	0.7105	{'precision': 0.640973630831643, 'recall': 0.7812113720642769, 'f1': 0.7041782729805014, 'number': 809}	{'precision': 0.2898550724637681, 'recall': 0.16806722689075632, 'f1': 0.2127659574468085, 'number': 119}	{'precision': 0.7317518248175182, 'recall': 0.7530516431924883, 'f1': 0.7422489588153632, 'number': 1065}	0.6760	0.7296	0.7017	0.7811
0.5543	6.0	60	0.6688	{'precision': 0.6625514403292181, 'recall': 0.796044499381953, 'f1': 0.7231892195395846, 'number': 809}	{'precision': 0.26732673267326734, 'recall': 0.226890756302521, 'f1': 0.24545454545454548, 'number': 119}	{'precision': 0.7412587412587412, 'recall': 0.7962441314553991, 'f1': 0.7677682209144409, 'number': 1065}	0.6852	0.7622	0.7216	0.8004
0.4829	7.0	70	0.6491	{'precision': 0.6635610766045549, 'recall': 0.792336217552534, 'f1': 0.7222535211267606, 'number': 809}	{'precision': 0.25471698113207547, 'recall': 0.226890756302521, 'f1': 0.24, 'number': 119}	{'precision': 0.7401372212692967, 'recall': 0.8103286384976526, 'f1': 0.7736441057821605, 'number': 1065}	0.6841	0.7682	0.7237	0.8056
0.4371	8.0	80	0.6419	{'precision': 0.6742502585315409, 'recall': 0.8059332509270705, 'f1': 0.7342342342342343, 'number': 809}	{'precision': 0.25210084033613445, 'recall': 0.25210084033613445, 'f1': 0.25210084033613445, 'number': 119}	{'precision': 0.7491228070175439, 'recall': 0.8018779342723005, 'f1': 0.7746031746031745, 'number': 1065}	0.6900	0.7707	0.7281	0.8062
0.3855	9.0	90	0.6560	{'precision': 0.6869747899159664, 'recall': 0.8084054388133498, 'f1': 0.7427597955706984, 'number': 809}	{'precision': 0.2711864406779661, 'recall': 0.2689075630252101, 'f1': 0.270042194092827, 'number': 119}	{'precision': 0.7871559633027523, 'recall': 0.8056338028169014, 'f1': 0.7962877030162413, 'number': 1065}	0.7148	0.7747	0.7436	0.8047
0.3511	10.0	100	0.6675	{'precision': 0.6853932584269663, 'recall': 0.8294190358467244, 'f1': 0.750559284116331, 'number': 809}	{'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}	{'precision': 0.7851239669421488, 'recall': 0.8028169014084507, 'f1': 0.7938718662952646, 'number': 1065}	0.7141	0.7832	0.7471	0.8062
0.3236	11.0	110	0.6729	{'precision': 0.7195121951219512, 'recall': 0.8022249690976514, 'f1': 0.7586206896551723, 'number': 809}	{'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119}	{'precision': 0.774798927613941, 'recall': 0.8140845070422535, 'f1': 0.7939560439560438, 'number': 1065}	0.7227	0.7807	0.7506	0.8045
0.307	12.0	120	0.6755	{'precision': 0.6757575757575758, 'recall': 0.826946847960445, 'f1': 0.7437465258476932, 'number': 809}	{'precision': 0.29365079365079366, 'recall': 0.31092436974789917, 'f1': 0.30204081632653057, 'number': 119}	{'precision': 0.7683363148479427, 'recall': 0.8065727699530516, 'f1': 0.7869903802107192, 'number': 1065}	0.7005	0.7852	0.7405	0.8052
0.2905	13.0	130	0.6712	{'precision': 0.6970021413276232, 'recall': 0.8046971569839307, 'f1': 0.7469879518072289, 'number': 809}	{'precision': 0.3007518796992481, 'recall': 0.33613445378151263, 'f1': 0.31746031746031744, 'number': 119}	{'precision': 0.7817028985507246, 'recall': 0.8103286384976526, 'f1': 0.7957584140156754, 'number': 1065}	0.7158	0.7797	0.7464	0.8067
0.2734	14.0	140	0.6758	{'precision': 0.6912681912681913, 'recall': 0.8220024721878862, 'f1': 0.7509881422924901, 'number': 809}	{'precision': 0.3089430894308943, 'recall': 0.31932773109243695, 'f1': 0.3140495867768595, 'number': 119}	{'precision': 0.7850045167118338, 'recall': 0.815962441314554, 'f1': 0.8001841620626151, 'number': 1065}	0.7172	0.7888	0.7513	0.8097
0.2672	15.0	150	0.6741	{'precision': 0.6960167714884696, 'recall': 0.8207663782447466, 'f1': 0.7532614861032332, 'number': 809}	{'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}	{'precision': 0.7824529991047449, 'recall': 0.8206572769953052, 'f1': 0.8010999083409717, 'number': 1065}	0.7178	0.7913	0.7527	0.8085

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

mathewchris96/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mathewchris96/
layoutlm-funsd