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trng1305/
layoutlm-sroie

Name: layoutlm-sroie
Availability: InStock

generated_from_trainer

autotrain_compatible

endpoints_compatible

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layoutlm-sroie

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: 0.0325
Address: {'precision': 0.9044943820224719, 'recall': 0.9279538904899135, 'f1': 0.9160739687055477, 'number': 347}
Company: {'precision': 0.9196675900277008, 'recall': 0.9567723342939481, 'f1': 0.9378531073446328, 'number': 347}
Date: {'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}
Total: {'precision': 0.8913649025069638, 'recall': 0.9221902017291066, 'f1': 0.9065155807365438, 'number': 347}
Overall Precision: 0.9242
Overall Recall: 0.9488
Overall F1: 0.9364
Overall Accuracy: 0.9947

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
0.4787	1.0	40	0.1051	{'precision': 0.8115183246073299, 'recall': 0.8933717579250721, 'f1': 0.850480109739369, 'number': 347}	{'precision': 0.6813725490196079, 'recall': 0.8011527377521613, 'f1': 0.7364238410596026, 'number': 347}	{'precision': 0.7438423645320197, 'recall': 0.8703170028818443, 'f1': 0.8021248339973439, 'number': 347}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 347}	0.7441	0.6412	0.6889	0.9705
0.0698	2.0	80	0.0453	{'precision': 0.848, 'recall': 0.9164265129682997, 'f1': 0.8808864265927978, 'number': 347}	{'precision': 0.8080808080808081, 'recall': 0.9221902017291066, 'f1': 0.8613728129205921, 'number': 347}	{'precision': 0.9281767955801105, 'recall': 0.968299711815562, 'f1': 0.9478138222849083, 'number': 347}	{'precision': 0.6992481203007519, 'recall': 0.8040345821325648, 'f1': 0.7479892761394101, 'number': 347}	0.8179	0.9027	0.8582	0.9882
0.0326	3.0	120	0.0317	{'precision': 0.8763736263736264, 'recall': 0.9193083573487032, 'f1': 0.8973277074542897, 'number': 347}	{'precision': 0.888283378746594, 'recall': 0.9394812680115274, 'f1': 0.9131652661064427, 'number': 347}	{'precision': 0.9713467048710601, 'recall': 0.9769452449567724, 'f1': 0.9741379310344828, 'number': 347}	{'precision': 0.8259668508287292, 'recall': 0.861671469740634, 'f1': 0.8434414668547249, 'number': 347}	0.8897	0.9244	0.9067	0.9928
0.0222	4.0	160	0.0333	{'precision': 0.8922651933701657, 'recall': 0.930835734870317, 'f1': 0.9111424541607898, 'number': 347}	{'precision': 0.8983516483516484, 'recall': 0.9423631123919308, 'f1': 0.919831223628692, 'number': 347}	{'precision': 0.9912280701754386, 'recall': 0.9769452449567724, 'f1': 0.9840348330914369, 'number': 347}	{'precision': 0.7348837209302326, 'recall': 0.9106628242074928, 'f1': 0.8133848133848134, 'number': 347}	0.8712	0.9402	0.9044	0.9921
0.0185	5.0	200	0.0288	{'precision': 0.9209039548022598, 'recall': 0.9394812680115274, 'f1': 0.9300998573466476, 'number': 347}	{'precision': 0.8856382978723404, 'recall': 0.9596541786743515, 'f1': 0.921161825726141, 'number': 347}	{'precision': 0.991304347826087, 'recall': 0.9855907780979827, 'f1': 0.9884393063583815, 'number': 347}	{'precision': 0.8547945205479452, 'recall': 0.899135446685879, 'f1': 0.8764044943820225, 'number': 347}	0.9118	0.9460	0.9286	0.9938
0.0141	6.0	240	0.0269	{'precision': 0.8991596638655462, 'recall': 0.9250720461095101, 'f1': 0.9119318181818182, 'number': 347}	{'precision': 0.9108635097493036, 'recall': 0.9423631123919308, 'f1': 0.926345609065156, 'number': 347}	{'precision': 0.9884393063583815, 'recall': 0.9855907780979827, 'f1': 0.987012987012987, 'number': 347}	{'precision': 0.8795518207282913, 'recall': 0.9048991354466859, 'f1': 0.8920454545454546, 'number': 347}	0.9190	0.9395	0.9291	0.9944
0.0117	7.0	280	0.0281	{'precision': 0.9178470254957507, 'recall': 0.9337175792507204, 'f1': 0.9257142857142857, 'number': 347}	{'precision': 0.9138888888888889, 'recall': 0.9481268011527377, 'f1': 0.9306930693069307, 'number': 347}	{'precision': 0.9855907780979827, 'recall': 0.9855907780979827, 'f1': 0.9855907780979827, 'number': 347}	{'precision': 0.8985507246376812, 'recall': 0.8933717579250721, 'f1': 0.8959537572254335, 'number': 347}	0.9288	0.9402	0.9345	0.9945
0.0104	8.0	320	0.0313	{'precision': 0.9101123595505618, 'recall': 0.9337175792507204, 'f1': 0.9217638691322901, 'number': 347}	{'precision': 0.9043715846994536, 'recall': 0.9538904899135446, 'f1': 0.9284712482468442, 'number': 347}	{'precision': 0.9717514124293786, 'recall': 0.9913544668587896, 'f1': 0.9814550641940086, 'number': 347}	{'precision': 0.868632707774799, 'recall': 0.9337175792507204, 'f1': 0.8999999999999999, 'number': 347}	0.9130	0.9532	0.9327	0.9941
0.009	9.0	360	0.0282	{'precision': 0.9204545454545454, 'recall': 0.9337175792507204, 'f1': 0.927038626609442, 'number': 347}	{'precision': 0.9171270718232044, 'recall': 0.9567723342939481, 'f1': 0.9365303244005642, 'number': 347}	{'precision': 0.9828571428571429, 'recall': 0.9913544668587896, 'f1': 0.9870875179340028, 'number': 347}	{'precision': 0.8885793871866295, 'recall': 0.9193083573487032, 'f1': 0.9036827195467422, 'number': 347}	0.9269	0.9503	0.9385	0.9949
0.0081	10.0	400	0.0313	{'precision': 0.9047619047619048, 'recall': 0.930835734870317, 'f1': 0.9176136363636365, 'number': 347}	{'precision': 0.9217877094972067, 'recall': 0.9510086455331412, 'f1': 0.9361702127659575, 'number': 347}	{'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}	{'precision': 0.8974358974358975, 'recall': 0.9077809798270894, 'f1': 0.9025787965616047, 'number': 347}	0.9265	0.9445	0.9354	0.9945
0.0064	11.0	440	0.0318	{'precision': 0.9047619047619048, 'recall': 0.930835734870317, 'f1': 0.9176136363636365, 'number': 347}	{'precision': 0.9138888888888889, 'recall': 0.9481268011527377, 'f1': 0.9306930693069307, 'number': 347}	{'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}	{'precision': 0.8791208791208791, 'recall': 0.9221902017291066, 'f1': 0.90014064697609, 'number': 347}	0.9196	0.9474	0.9333	0.9944
0.0063	12.0	480	0.0335	{'precision': 0.8994413407821229, 'recall': 0.9279538904899135, 'f1': 0.9134751773049646, 'number': 347}	{'precision': 0.9088397790055248, 'recall': 0.9481268011527377, 'f1': 0.928067700987306, 'number': 347}	{'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}	{'precision': 0.8839779005524862, 'recall': 0.9221902017291066, 'f1': 0.9026798307475318, 'number': 347}	0.9182	0.9467	0.9322	0.9943
0.0054	13.0	520	0.0312	{'precision': 0.9070422535211268, 'recall': 0.9279538904899135, 'f1': 0.9173789173789173, 'number': 347}	{'precision': 0.9194444444444444, 'recall': 0.9538904899135446, 'f1': 0.9363507779349363, 'number': 347}	{'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}	{'precision': 0.8839779005524862, 'recall': 0.9221902017291066, 'f1': 0.9026798307475318, 'number': 347}	0.9229	0.9481	0.9353	0.9947
0.0054	14.0	560	0.0326	{'precision': 0.9044943820224719, 'recall': 0.9279538904899135, 'f1': 0.9160739687055477, 'number': 347}	{'precision': 0.9222222222222223, 'recall': 0.9567723342939481, 'f1': 0.9391796322489392, 'number': 347}	{'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}	{'precision': 0.8910614525139665, 'recall': 0.9193083573487032, 'f1': 0.9049645390070922, 'number': 347}	0.9248	0.9481	0.9363	0.9946
0.0048	15.0	600	0.0325	{'precision': 0.9044943820224719, 'recall': 0.9279538904899135, 'f1': 0.9160739687055477, 'number': 347}	{'precision': 0.9196675900277008, 'recall': 0.9567723342939481, 'f1': 0.9378531073446328, 'number': 347}	{'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}	{'precision': 0.8913649025069638, 'recall': 0.9221902017291066, 'f1': 0.9065155807365438, 'number': 347}	0.9242	0.9488	0.9364	0.9947

Framework versions

Transformers 4.28.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.13.3

trng1305/layoutlm-sroie

layoutlm-sroie

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

trng1305/
layoutlm-sroie