layoutlm-sroie

npnam693/layoutlm-sroie

layoutlm-sroie is a token classification model from npnam693. Use it when you need labels on individual words, such as names. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Nov 17, 2023
Updated: Nov 18, 2023
SHA: 200dd018

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Datasets

sroie

Model card

From the Hugging Face model README

layoutlm-sroie

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the sroie dataset. It achieves the following results on the evaluation set:

Loss: 0.0363
Address: {'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}
Company: {'precision': 0.904891304347826, 'recall': 0.9596541786743515, 'f1': 0.9314685314685315, 'number': 347}
Date: {'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}
Total: {'precision': 0.8155080213903744, 'recall': 0.8789625360230547, 'f1': 0.8460471567267684, 'number': 347}
Overall Precision: 0.9017
Overall Recall: 0.9388
Overall F1: 0.9199
Overall Accuracy: 0.9930

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	Address	Company	Date	Total	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.5189	1.0	40	0.1280	{'precision': 0.7891891891891892, 'recall': 0.8414985590778098, 'f1': 0.8145048814504882, 'number': 347}	{'precision': 0.6987012987012987, 'recall': 0.7752161383285303, 'f1': 0.7349726775956286, 'number': 347}	{'precision': 0.6651982378854625, 'recall': 0.8703170028818443, 'f1': 0.7540574282147315, 'number': 347}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 347}	0.7138	0.6218	0.6646	0.9650
0.0849	2.0	80	0.0558	{'precision': 0.8753462603878116, 'recall': 0.9106628242074928, 'f1': 0.8926553672316384, 'number': 347}	{'precision': 0.8102189781021898, 'recall': 0.9596541786743515, 'f1': 0.8786279683377309, 'number': 347}	{'precision': 0.9178082191780822, 'recall': 0.9654178674351584, 'f1': 0.9410112359550562, 'number': 347}	{'precision': 0.5179282868525896, 'recall': 0.3746397694524496, 'f1': 0.43478260869565216, 'number': 347}	0.8026	0.8026	0.8026	0.9851
0.0447	3.0	120	0.0435	{'precision': 0.8997214484679665, 'recall': 0.930835734870317, 'f1': 0.9150141643059491, 'number': 347}	{'precision': 0.8954423592493298, 'recall': 0.962536023054755, 'f1': 0.9277777777777777, 'number': 347}	{'precision': 0.96045197740113, 'recall': 0.9798270893371758, 'f1': 0.9700427960057061, 'number': 347}	{'precision': 0.6222222222222222, 'recall': 0.6455331412103746, 'f1': 0.6336633663366337, 'number': 347}	0.8444	0.8797	0.8617	0.9890
0.0318	4.0	160	0.0347	{'precision': 0.8777777777777778, 'recall': 0.9106628242074928, 'f1': 0.8939179632248939, 'number': 347}	{'precision': 0.9153005464480874, 'recall': 0.9654178674351584, 'f1': 0.9396914446002805, 'number': 347}	{'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}	{'precision': 0.671957671957672, 'recall': 0.7319884726224783, 'f1': 0.7006896551724137, 'number': 347}	0.8608	0.8999	0.8799	0.9910
0.0245	5.0	200	0.0360	{'precision': 0.8885793871866295, 'recall': 0.9193083573487032, 'f1': 0.9036827195467422, 'number': 347}	{'precision': 0.8909574468085106, 'recall': 0.9654178674351584, 'f1': 0.9266943291839556, 'number': 347}	{'precision': 0.9913294797687862, 'recall': 0.9884726224783862, 'f1': 0.98989898989899, 'number': 347}	{'precision': 0.7873754152823921, 'recall': 0.6829971181556196, 'f1': 0.7314814814814815, 'number': 347}	0.8929	0.8890	0.8910	0.9910
0.0171	6.0	240	0.0325	{'precision': 0.8932584269662921, 'recall': 0.9164265129682997, 'f1': 0.9046941678520626, 'number': 347}	{'precision': 0.912568306010929, 'recall': 0.962536023054755, 'f1': 0.9368863955119215, 'number': 347}	{'precision': 0.991304347826087, 'recall': 0.9855907780979827, 'f1': 0.9884393063583815, 'number': 347}	{'precision': 0.823170731707317, 'recall': 0.7780979827089337, 'f1': 0.8, 'number': 347}	0.9061	0.9107	0.9084	0.9926
0.0133	7.0	280	0.0352	{'precision': 0.8969359331476323, 'recall': 0.9279538904899135, 'f1': 0.9121813031161472, 'number': 347}	{'precision': 0.9103260869565217, 'recall': 0.9654178674351584, 'f1': 0.937062937062937, 'number': 347}	{'precision': 0.9885057471264368, 'recall': 0.9913544668587896, 'f1': 0.9899280575539569, 'number': 347}	{'precision': 0.7801608579088471, 'recall': 0.8386167146974063, 'f1': 0.8083333333333332, 'number': 347}	0.8923	0.9308	0.9111	0.9922
0.013	8.0	320	0.0338	{'precision': 0.889196675900277, 'recall': 0.9250720461095101, 'f1': 0.9067796610169492, 'number': 347}	{'precision': 0.9103260869565217, 'recall': 0.9654178674351584, 'f1': 0.937062937062937, 'number': 347}	{'precision': 0.9885057471264368, 'recall': 0.9913544668587896, 'f1': 0.9899280575539569, 'number': 347}	{'precision': 0.7887700534759359, 'recall': 0.8501440922190202, 'f1': 0.8183079056865465, 'number': 347}	0.8925	0.9330	0.9123	0.9927
0.0105	9.0	360	0.0378	{'precision': 0.8885793871866295, 'recall': 0.9193083573487032, 'f1': 0.9036827195467422, 'number': 347}	{'precision': 0.9081081081081082, 'recall': 0.968299711815562, 'f1': 0.9372384937238494, 'number': 347}	{'precision': 0.9913294797687862, 'recall': 0.9884726224783862, 'f1': 0.98989898989899, 'number': 347}	{'precision': 0.8096590909090909, 'recall': 0.8213256484149856, 'f1': 0.8154506437768241, 'number': 347}	0.8991	0.9244	0.9115	0.9923
0.0094	10.0	400	0.0353	{'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}	{'precision': 0.904891304347826, 'recall': 0.9596541786743515, 'f1': 0.9314685314685315, 'number': 347}	{'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}	{'precision': 0.8142076502732241, 'recall': 0.8587896253602305, 'f1': 0.8359046283309958, 'number': 347}	0.9019	0.9337	0.9175	0.9929
0.0078	11.0	440	0.0373	{'precision': 0.8938547486033519, 'recall': 0.9221902017291066, 'f1': 0.9078014184397163, 'number': 347}	{'precision': 0.9098360655737705, 'recall': 0.9596541786743515, 'f1': 0.9340813464235624, 'number': 347}	{'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}	{'precision': 0.8150134048257373, 'recall': 0.8760806916426513, 'f1': 0.8444444444444444, 'number': 347}	0.9010	0.9373	0.9188	0.9928
0.0074	12.0	480	0.0379	{'precision': 0.8994413407821229, 'recall': 0.9279538904899135, 'f1': 0.9134751773049646, 'number': 347}	{'precision': 0.9128065395095368, 'recall': 0.9654178674351584, 'f1': 0.938375350140056, 'number': 347}	{'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}	{'precision': 0.835195530726257, 'recall': 0.861671469740634, 'f1': 0.8482269503546098, 'number': 347}	0.9091	0.9366	0.9226	0.9931
0.007	13.0	520	0.0357	{'precision': 0.9019607843137255, 'recall': 0.9279538904899135, 'f1': 0.9147727272727272, 'number': 347}	{'precision': 0.9024390243902439, 'recall': 0.9596541786743515, 'f1': 0.9301675977653631, 'number': 347}	{'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}	{'precision': 0.8328767123287671, 'recall': 0.8760806916426513, 'f1': 0.853932584269663, 'number': 347}	0.9061	0.9388	0.9222	0.9932
0.0069	14.0	560	0.0361	{'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}	{'precision': 0.9051490514905149, 'recall': 0.962536023054755, 'f1': 0.9329608938547486, 'number': 347}	{'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}	{'precision': 0.8046875, 'recall': 0.8904899135446686, 'f1': 0.8454172366621068, 'number': 347}	0.8984	0.9424	0.9198	0.9930
0.0065	15.0	600	0.0363	{'precision': 0.901685393258427, 'recall': 0.9250720461095101, 'f1': 0.9132290184921764, 'number': 347}	{'precision': 0.904891304347826, 'recall': 0.9596541786743515, 'f1': 0.9314685314685315, 'number': 347}	{'precision': 0.9913544668587896, 'recall': 0.9913544668587896, 'f1': 0.9913544668587896, 'number': 347}	{'precision': 0.8155080213903744, 'recall': 0.8789625360230547, 'f1': 0.8460471567267684, 'number': 347}	0.9017	0.9388	0.9199	0.9930

Framework versions

Transformers 4.28.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Nov 17, 2023
Updated: Nov 18, 2023