Loss: 0.6559
Answer: {'precision': 0.7160220994475138, 'recall': 0.8009888751545118, 'f1': 0.7561260210035006, 'number': 809}
Header: {'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}
Question: {'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065}
Overall Precision: 0.7215
Overall Recall: 0.7863
Overall F1: 0.7525
Overall Accuracy: 0.8165

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
1.8319	1.0	10	1.6114	{'precision': 0.02668213457076566, 'recall': 0.02843016069221261, 'f1': 0.02752842609216038, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.21885521885521886, 'recall': 0.18309859154929578, 'f1': 0.19938650306748468, 'number': 1065}	0.1244	0.1094	0.1164	0.3478
1.4535	2.0	20	1.2624	{'precision': 0.2141119221411192, 'recall': 0.21755253399258342, 'f1': 0.2158185162477008, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.45236250968241676, 'recall': 0.5483568075117371, 'f1': 0.4957555178268252, 'number': 1065}	0.3597	0.3813	0.3702	0.5768
1.099	3.0	30	0.9496	{'precision': 0.46866840731070497, 'recall': 0.4437577255871446, 'f1': 0.4558730158730159, 'number': 809}	{'precision': 0.05405405405405406, 'recall': 0.01680672268907563, 'f1': 0.02564102564102564, 'number': 119}	{'precision': 0.6174957118353345, 'recall': 0.676056338028169, 'f1': 0.6454504706409682, 'number': 1065}	0.5490	0.5424	0.5457	0.7045
0.8218	4.0	40	0.7695	{'precision': 0.5814606741573034, 'recall': 0.7676143386897404, 'f1': 0.6616941928609482, 'number': 809}	{'precision': 0.1935483870967742, 'recall': 0.10084033613445378, 'f1': 0.13259668508287292, 'number': 119}	{'precision': 0.6691983122362869, 'recall': 0.7446009389671362, 'f1': 0.7048888888888889, 'number': 1065}	0.6160	0.7155	0.6620	0.7620
0.6633	5.0	50	0.7008	{'precision': 0.6237006237006237, 'recall': 0.7416563658838071, 'f1': 0.6775832862789385, 'number': 809}	{'precision': 0.2571428571428571, 'recall': 0.15126050420168066, 'f1': 0.19047619047619044, 'number': 119}	{'precision': 0.7088055797733217, 'recall': 0.7633802816901408, 'f1': 0.7350813743218807, 'number': 1065}	0.6567	0.7180	0.6860	0.7819
0.5651	6.0	60	0.6659	{'precision': 0.6533192834562698, 'recall': 0.7663782447466008, 'f1': 0.7053469852104665, 'number': 809}	{'precision': 0.2564102564102564, 'recall': 0.25210084033613445, 'f1': 0.2542372881355932, 'number': 119}	{'precision': 0.7251655629139073, 'recall': 0.8225352112676056, 'f1': 0.7707875054993402, 'number': 1065}	0.6711	0.7657	0.7153	0.7976
0.4862	7.0	70	0.6514	{'precision': 0.6496815286624203, 'recall': 0.7564894932014833, 'f1': 0.6990291262135921, 'number': 809}	{'precision': 0.30927835051546393, 'recall': 0.25210084033613445, 'f1': 0.2777777777777778, 'number': 119}	{'precision': 0.7352206494587843, 'recall': 0.8291079812206573, 'f1': 0.7793468667255075, 'number': 1065}	0.6808	0.7652	0.7205	0.8038
0.4421	8.0	80	0.6342	{'precision': 0.6720085470085471, 'recall': 0.7775030902348579, 'f1': 0.7209169054441262, 'number': 809}	{'precision': 0.3017241379310345, 'recall': 0.29411764705882354, 'f1': 0.29787234042553185, 'number': 119}	{'precision': 0.7461928934010152, 'recall': 0.828169014084507, 'f1': 0.7850467289719626, 'number': 1065}	0.6920	0.7757	0.7315	0.8087
0.3898	9.0	90	0.6485	{'precision': 0.7045203969128997, 'recall': 0.7898640296662547, 'f1': 0.7447552447552448, 'number': 809}	{'precision': 0.32038834951456313, 'recall': 0.2773109243697479, 'f1': 0.29729729729729737, 'number': 119}	{'precision': 0.7669902912621359, 'recall': 0.815962441314554, 'f1': 0.7907188353048227, 'number': 1065}	0.7191	0.7732	0.7452	0.8099
0.3531	10.0	100	0.6380	{'precision': 0.7058177826564215, 'recall': 0.7948084054388134, 'f1': 0.7476744186046511, 'number': 809}	{'precision': 0.33980582524271846, 'recall': 0.29411764705882354, 'f1': 0.31531531531531537, 'number': 119}	{'precision': 0.7579672695951766, 'recall': 0.8262910798122066, 'f1': 0.7906558849955077, 'number': 1065}	0.7163	0.7817	0.7476	0.8155
0.3226	11.0	110	0.6484	{'precision': 0.72, 'recall': 0.8009888751545118, 'f1': 0.7583382094792276, 'number': 809}	{'precision': 0.2962962962962963, 'recall': 0.2689075630252101, 'f1': 0.28193832599118945, 'number': 119}	{'precision': 0.7819481680071493, 'recall': 0.8215962441314554, 'f1': 0.8012820512820512, 'number': 1065}	0.7311	0.7802	0.7549	0.8171
0.3066	12.0	120	0.6399	{'precision': 0.7007616974972797, 'recall': 0.796044499381953, 'f1': 0.7453703703703702, 'number': 809}	{'precision': 0.3181818181818182, 'recall': 0.29411764705882354, 'f1': 0.3056768558951965, 'number': 119}	{'precision': 0.7610544217687075, 'recall': 0.8403755868544601, 'f1': 0.7987505577867025, 'number': 1065}	0.7138	0.7898	0.7499	0.8195
0.2932	13.0	130	0.6628	{'precision': 0.7155555555555555, 'recall': 0.796044499381953, 'f1': 0.7536571094207138, 'number': 809}	{'precision': 0.288, 'recall': 0.3025210084033613, 'f1': 0.2950819672131147, 'number': 119}	{'precision': 0.7783783783783784, 'recall': 0.8112676056338028, 'f1': 0.7944827586206896, 'number': 1065}	0.7232	0.7747	0.7481	0.8163
0.2739	14.0	140	0.6550	{'precision': 0.7190265486725663, 'recall': 0.8034610630407911, 'f1': 0.7589025102159953, 'number': 809}	{'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119}	{'precision': 0.766695576756288, 'recall': 0.8300469483568075, 'f1': 0.7971145175834085, 'number': 1065}	0.7232	0.7878	0.7541	0.8173
0.2715	15.0	150	0.6559	{'precision': 0.7160220994475138, 'recall': 0.8009888751545118, 'f1': 0.7561260210035006, 'number': 809}	{'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}	{'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065}	0.7215	0.7863	0.7525	0.8165

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

barryjdevereux/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

barryjdevereux/
layoutlm-funsd