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trng1305/
layoutlm-sroie-dacn

Name: layoutlm-sroie-dacn
Availability: InStock

generated_from_trainer

endpoints_compatible

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layoutlm-sroie-dacn

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1268
Address: {'precision': 0.9905708460754332, 'recall': 0.9948809828512926, 'f1': 0.9927212361128847, 'number': 3907}
Company: {'precision': 0.9704530531845043, 'recall': 0.9912810194500336, 'f1': 0.9807564698075647, 'number': 1491}
Date: {'precision': 1.0, 'recall': 0.9929906542056075, 'f1': 0.9964830011723329, 'number': 428}
Total: {'precision': 0.8861788617886179, 'recall': 0.8814016172506739, 'f1': 0.8837837837837837, 'number': 371}
Overall Precision: 0.9801
Overall Recall: 0.9871
Overall F1: 0.9836
Overall Accuracy: 0.9950

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: 20
label_smoothing_factor: 0.02

Training results

Training Loss	Epoch	Step	Validation Loss	Address	Company	Date	Total	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.3877	1.0	40	0.1597	{'precision': 0.9882921863069484, 'recall': 0.993857179421551, 'f1': 0.9910668708524758, 'number': 3907}	{'precision': 0.8616279069767442, 'recall': 0.993963782696177, 'f1': 0.9230769230769231, 'number': 1491}	{'precision': 0.838, 'recall': 0.9789719626168224, 'f1': 0.9030172413793104, 'number': 428}	{'precision': 0.9090909090909091, 'recall': 0.026954177897574125, 'f1': 0.05235602094240838, 'number': 371}	0.9406	0.9350	0.9378	0.9812
0.1439	2.0	80	0.1310	{'precision': 0.9940934771443246, 'recall': 0.9907857691323266, 'f1': 0.9924368670683246, 'number': 3907}	{'precision': 0.9517994858611826, 'recall': 0.9932930918846412, 'f1': 0.9721037085658024, 'number': 1491}	{'precision': 0.9789719626168224, 'recall': 0.9789719626168224, 'f1': 0.9789719626168224, 'number': 428}	{'precision': 0.7128205128205128, 'recall': 0.7493261455525606, 'f1': 0.7306176084099868, 'number': 371}	0.9651	0.9761	0.9706	0.9911
0.1267	3.0	120	0.1283	{'precision': 0.9890557393738865, 'recall': 0.9946250319938572, 'f1': 0.9918325676365493, 'number': 3907}	{'precision': 0.9647979139504563, 'recall': 0.9926224010731053, 'f1': 0.978512396694215, 'number': 1491}	{'precision': 0.9952718676122931, 'recall': 0.9836448598130841, 'f1': 0.9894242068155112, 'number': 428}	{'precision': 0.783375314861461, 'recall': 0.8382749326145552, 'f1': 0.8098958333333334, 'number': 371}	0.9706	0.9840	0.9772	0.9931
0.12	4.0	160	0.1280	{'precision': 0.984070796460177, 'recall': 0.9961607371384694, 'f1': 0.9900788603408802, 'number': 3907}	{'precision': 0.9678899082568807, 'recall': 0.9906103286384976, 'f1': 0.9791183294663572, 'number': 1491}	{'precision': 0.9929411764705882, 'recall': 0.985981308411215, 'f1': 0.9894490035169988, 'number': 428}	{'precision': 0.8219895287958116, 'recall': 0.8463611859838275, 'f1': 0.8339973439575034, 'number': 371}	0.9709	0.9852	0.9780	0.9932
0.1167	5.0	200	0.1254	{'precision': 0.9903061224489796, 'recall': 0.9936012285641157, 'f1': 0.9919509390571101, 'number': 3907}	{'precision': 0.9692206941715783, 'recall': 0.9926224010731053, 'f1': 0.9807819748177601, 'number': 1491}	{'precision': 0.9929411764705882, 'recall': 0.985981308411215, 'f1': 0.9894490035169988, 'number': 428}	{'precision': 0.8445040214477212, 'recall': 0.8490566037735849, 'f1': 0.8467741935483872, 'number': 371}	0.9766	0.9842	0.9804	0.9940
0.1128	6.0	240	0.1273	{'precision': 0.9865584580268831, 'recall': 0.9956488354235987, 'f1': 0.9910828025477707, 'number': 3907}	{'precision': 0.9698952879581152, 'recall': 0.993963782696177, 'f1': 0.9817820470354421, 'number': 1491}	{'precision': 1.0, 'recall': 0.9789719626168224, 'f1': 0.9893742621015349, 'number': 428}	{'precision': 0.8575268817204301, 'recall': 0.8598382749326146, 'f1': 0.8586810228802152, 'number': 371}	0.9757	0.9860	0.9808	0.9941
0.1119	7.0	280	0.1250	{'precision': 0.9943604204050244, 'recall': 0.9928333759918095, 'f1': 0.9935963114754098, 'number': 3907}	{'precision': 0.9717477003942181, 'recall': 0.9919517102615694, 'f1': 0.9817457683372054, 'number': 1491}	{'precision': 0.9976415094339622, 'recall': 0.9883177570093458, 'f1': 0.9929577464788731, 'number': 428}	{'precision': 0.9037900874635568, 'recall': 0.8355795148247979, 'f1': 0.8683473389355741, 'number': 371}	0.9840	0.9829	0.9835	0.9950
0.1111	8.0	320	0.1264	{'precision': 0.9900586286005608, 'recall': 0.9941131302789864, 'f1': 0.9920817369093232, 'number': 3907}	{'precision': 0.9654498044328553, 'recall': 0.9932930918846412, 'f1': 0.9791735537190083, 'number': 1491}	{'precision': 0.9953161592505855, 'recall': 0.9929906542056075, 'f1': 0.9941520467836257, 'number': 428}	{'precision': 0.8904494382022472, 'recall': 0.8544474393530997, 'f1': 0.8720770288858323, 'number': 371}	0.9787	0.9855	0.9821	0.9946
0.1097	9.0	360	0.1264	{'precision': 0.9890529531568228, 'recall': 0.9943690811364219, 'f1': 0.9917038927887685, 'number': 3907}	{'precision': 0.9679738562091503, 'recall': 0.9932930918846412, 'f1': 0.9804700430321085, 'number': 1491}	{'precision': 1.0, 'recall': 0.9883177570093458, 'f1': 0.9941245593419507, 'number': 428}	{'precision': 0.8919667590027701, 'recall': 0.8679245283018868, 'f1': 0.8797814207650273, 'number': 371}	0.9790	0.9861	0.9826	0.9947
0.1091	10.0	400	0.1256	{'precision': 0.9918346516968615, 'recall': 0.9948809828512926, 'f1': 0.9933554817275747, 'number': 3907}	{'precision': 0.972972972972973, 'recall': 0.9899396378269618, 'f1': 0.9813829787234043, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.898876404494382, 'recall': 0.862533692722372, 'f1': 0.8803301237964237, 'number': 371}	0.9825	0.9855	0.9840	0.9951
0.1085	11.0	440	0.1270	{'precision': 0.9913287426676868, 'recall': 0.9948809828512926, 'f1': 0.9931016862544711, 'number': 3907}	{'precision': 0.9673416067929458, 'recall': 0.9932930918846412, 'f1': 0.9801455989410985, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8828337874659401, 'recall': 0.8733153638814016, 'f1': 0.878048780487805, 'number': 371}	0.9797	0.9869	0.9833	0.9949
0.1089	12.0	480	0.1263	{'precision': 0.9903184713375797, 'recall': 0.9948809828512926, 'f1': 0.9925944841675179, 'number': 3907}	{'precision': 0.969301110385369, 'recall': 0.9953051643192489, 'f1': 0.9821310390469887, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.905982905982906, 'recall': 0.8571428571428571, 'f1': 0.8808864265927977, 'number': 371}	0.9811	0.9864	0.9837	0.9950
0.1081	13.0	520	0.1258	{'precision': 0.9913287426676868, 'recall': 0.9948809828512926, 'f1': 0.9931016862544711, 'number': 3907}	{'precision': 0.9711475409836066, 'recall': 0.9932930918846412, 'f1': 0.9820954907161804, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8885869565217391, 'recall': 0.8814016172506739, 'f1': 0.884979702300406, 'number': 371}	0.9809	0.9874	0.9842	0.9952
0.1081	14.0	560	0.1275	{'precision': 0.9903184713375797, 'recall': 0.9948809828512926, 'f1': 0.9925944841675179, 'number': 3907}	{'precision': 0.9655172413793104, 'recall': 0.9953051643192489, 'f1': 0.9801849405548217, 'number': 1491}	{'precision': 1.0, 'recall': 0.9906542056074766, 'f1': 0.9953051643192489, 'number': 428}	{'precision': 0.8837837837837837, 'recall': 0.8814016172506739, 'f1': 0.8825910931174089, 'number': 371}	0.9786	0.9879	0.9832	0.9949
0.108	15.0	600	0.1276	{'precision': 0.9900662251655629, 'recall': 0.9948809828512926, 'f1': 0.992467764585727, 'number': 3907}	{'precision': 0.961139896373057, 'recall': 0.9953051643192489, 'f1': 0.9779242174629325, 'number': 1491}	{'precision': 1.0, 'recall': 0.9929906542056075, 'f1': 0.9964830011723329, 'number': 428}	{'precision': 0.8937329700272479, 'recall': 0.8840970350404312, 'f1': 0.888888888888889, 'number': 371}	0.9780	0.9882	0.9831	0.9948
0.1077	16.0	640	0.1268	{'precision': 0.9900662251655629, 'recall': 0.9948809828512926, 'f1': 0.992467764585727, 'number': 3907}	{'precision': 0.965472312703583, 'recall': 0.993963782696177, 'f1': 0.9795109054857898, 'number': 1491}	{'precision': 1.0, 'recall': 0.9929906542056075, 'f1': 0.9964830011723329, 'number': 428}	{'precision': 0.8885869565217391, 'recall': 0.8814016172506739, 'f1': 0.884979702300406, 'number': 371}	0.9787	0.9877	0.9832	0.9949
0.1074	17.0	680	0.1266	{'precision': 0.9903184713375797, 'recall': 0.9948809828512926, 'f1': 0.9925944841675179, 'number': 3907}	{'precision': 0.9679529103989536, 'recall': 0.9926224010731053, 'f1': 0.980132450331126, 'number': 1491}	{'precision': 1.0, 'recall': 0.9929906542056075, 'f1': 0.9964830011723329, 'number': 428}	{'precision': 0.8922651933701657, 'recall': 0.8706199460916442, 'f1': 0.8813096862210095, 'number': 371}	0.9798	0.9868	0.9833	0.9949
0.1072	18.0	720	0.1266	{'precision': 0.9905708460754332, 'recall': 0.9948809828512926, 'f1': 0.9927212361128847, 'number': 3907}	{'precision': 0.9710716633793557, 'recall': 0.9906103286384976, 'f1': 0.9807436918990704, 'number': 1491}	{'precision': 1.0, 'recall': 0.9929906542056075, 'f1': 0.9964830011723329, 'number': 428}	{'precision': 0.8814016172506739, 'recall': 0.8814016172506739, 'f1': 0.8814016172506739, 'number': 371}	0.9800	0.9869	0.9834	0.9950
0.1071	19.0	760	0.1266	{'precision': 0.9905708460754332, 'recall': 0.9948809828512926, 'f1': 0.9927212361128847, 'number': 3907}	{'precision': 0.9704530531845043, 'recall': 0.9912810194500336, 'f1': 0.9807564698075647, 'number': 1491}	{'precision': 1.0, 'recall': 0.9929906542056075, 'f1': 0.9964830011723329, 'number': 428}	{'precision': 0.8858695652173914, 'recall': 0.8787061994609164, 'f1': 0.8822733423545331, 'number': 371}	0.9801	0.9869	0.9835	0.9950
0.1076	20.0	800	0.1268	{'precision': 0.9905708460754332, 'recall': 0.9948809828512926, 'f1': 0.9927212361128847, 'number': 3907}	{'precision': 0.9704530531845043, 'recall': 0.9912810194500336, 'f1': 0.9807564698075647, 'number': 1491}	{'precision': 1.0, 'recall': 0.9929906542056075, 'f1': 0.9964830011723329, 'number': 428}	{'precision': 0.8861788617886179, 'recall': 0.8814016172506739, 'f1': 0.8837837837837837, 'number': 371}	0.9801	0.9871	0.9836	0.9950

Framework versions

Transformers 4.28.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.13.3

trng1305/layoutlm-sroie-dacn

layoutlm-sroie-dacn

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

trng1305/
layoutlm-sroie-dacn