layoutlm-custom_no_text

uttam333/layoutlm-custom_no_text

layoutlm-custom_no_text is a token classification model from uttam333. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: layoutlm
Access: Public
Created: Jan 9, 2024
Updated: Jan 10, 2024
SHA: 3ef071e6

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Base models

microsoft/layoutlm-base-uncased

Model card

From the Hugging Face model README

layoutlm-custom_no_text

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: 0.5142
Noise: {'precision': 0.6764705882352942, 'recall': 0.6695205479452054, 'f1': 0.6729776247848538, 'number': 584}
Signal: {'precision': 0.629757785467128, 'recall': 0.6232876712328768, 'f1': 0.6265060240963856, 'number': 584}
Overall Precision: 0.6531
Overall Recall: 0.6464
Overall F1: 0.6497
Overall Accuracy: 0.9156

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Noise	Signal	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.5712	1.0	18	0.4356	{'precision': 0.3712374581939799, 'recall': 0.3801369863013699, 'f1': 0.3756345177664975, 'number': 584}	{'precision': 0.3294314381270903, 'recall': 0.3373287671232877, 'f1': 0.3333333333333333, 'number': 584}	0.3503	0.3587	0.3545	0.8008
0.4233	2.0	36	0.3745	{'precision': 0.4048964218455744, 'recall': 0.3681506849315068, 'f1': 0.38565022421524664, 'number': 584}	{'precision': 0.3483992467043315, 'recall': 0.3167808219178082, 'f1': 0.33183856502242154, 'number': 584}	0.3766	0.3425	0.3587	0.8287
0.3817	3.0	54	0.3632	{'precision': 0.45740740740740743, 'recall': 0.4229452054794521, 'f1': 0.43950177935943063, 'number': 584}	{'precision': 0.3851851851851852, 'recall': 0.3561643835616438, 'f1': 0.3701067615658363, 'number': 584}	0.4213	0.3896	0.4048	0.8413
0.3472	4.0	72	0.3133	{'precision': 0.5143953934740882, 'recall': 0.4589041095890411, 'f1': 0.4850678733031674, 'number': 584}	{'precision': 0.43378119001919385, 'recall': 0.386986301369863, 'f1': 0.40904977375565615, 'number': 584}	0.4741	0.4229	0.4471	0.8550
0.3132	5.0	90	0.3254	{'precision': 0.5112016293279023, 'recall': 0.4297945205479452, 'f1': 0.4669767441860465, 'number': 584}	{'precision': 0.4460285132382892, 'recall': 0.375, 'f1': 0.4074418604651162, 'number': 584}	0.4786	0.4024	0.4372	0.8525
0.282	6.0	108	0.3033	{'precision': 0.5387453874538746, 'recall': 0.5, 'f1': 0.5186500888099467, 'number': 584}	{'precision': 0.46863468634686345, 'recall': 0.4349315068493151, 'f1': 0.45115452930728245, 'number': 584}	0.5037	0.4675	0.4849	0.8656
0.2486	7.0	126	0.2827	{'precision': 0.5498220640569395, 'recall': 0.5291095890410958, 'f1': 0.5392670157068062, 'number': 584}	{'precision': 0.5071174377224199, 'recall': 0.488013698630137, 'f1': 0.49738219895287955, 'number': 584}	0.5285	0.5086	0.5183	0.8773
0.2276	8.0	144	0.2798	{'precision': 0.5597826086956522, 'recall': 0.5291095890410958, 'f1': 0.5440140845070423, 'number': 584}	{'precision': 0.5235507246376812, 'recall': 0.4948630136986301, 'f1': 0.5088028169014084, 'number': 584}	0.5417	0.5120	0.5264	0.8793
0.197	9.0	162	0.2778	{'precision': 0.5948905109489051, 'recall': 0.5582191780821918, 'f1': 0.5759717314487632, 'number': 584}	{'precision': 0.5602189781021898, 'recall': 0.5256849315068494, 'f1': 0.5424028268551236, 'number': 584}	0.5776	0.5420	0.5592	0.8891
0.1812	10.0	180	0.2932	{'precision': 0.5907407407407408, 'recall': 0.5462328767123288, 'f1': 0.5676156583629893, 'number': 584}	{'precision': 0.5666666666666667, 'recall': 0.523972602739726, 'f1': 0.5444839857651246, 'number': 584}	0.5787	0.5351	0.5560	0.8888
0.1611	11.0	198	0.2785	{'precision': 0.6156648451730419, 'recall': 0.5787671232876712, 'f1': 0.5966460723742276, 'number': 584}	{'precision': 0.5719489981785064, 'recall': 0.5376712328767124, 'f1': 0.5542806707855252, 'number': 584}	0.5938	0.5582	0.5755	0.8991
0.1441	12.0	216	0.2738	{'precision': 0.6263537906137184, 'recall': 0.5941780821917808, 'f1': 0.6098418277680141, 'number': 584}	{'precision': 0.5776173285198556, 'recall': 0.547945205479452, 'f1': 0.562390158172232, 'number': 584}	0.6020	0.5711	0.5861	0.9016
0.1294	13.0	234	0.3072	{'precision': 0.6201413427561837, 'recall': 0.601027397260274, 'f1': 0.6104347826086957, 'number': 584}	{'precision': 0.5795053003533569, 'recall': 0.5616438356164384, 'f1': 0.5704347826086956, 'number': 584}	0.5998	0.5813	0.5904	0.8989
0.1218	14.0	252	0.2963	{'precision': 0.629695885509839, 'recall': 0.6027397260273972, 'f1': 0.6159230096237971, 'number': 584}	{'precision': 0.5849731663685152, 'recall': 0.559931506849315, 'f1': 0.5721784776902886, 'number': 584}	0.6073	0.5813	0.5941	0.9030
0.1032	15.0	270	0.3365	{'precision': 0.6106194690265486, 'recall': 0.5907534246575342, 'f1': 0.6005221932114881, 'number': 584}	{'precision': 0.5681415929203539, 'recall': 0.5496575342465754, 'f1': 0.5587467362924282, 'number': 584}	0.5894	0.5702	0.5796	0.8991
0.0981	16.0	288	0.3342	{'precision': 0.631858407079646, 'recall': 0.6113013698630136, 'f1': 0.6214099216710183, 'number': 584}	{'precision': 0.5893805309734513, 'recall': 0.5702054794520548, 'f1': 0.5796344647519582, 'number': 584}	0.6106	0.5908	0.6005	0.9039
0.0844	17.0	306	0.3543	{'precision': 0.6502636203866432, 'recall': 0.6335616438356164, 'f1': 0.6418039895923676, 'number': 584}	{'precision': 0.5957820738137083, 'recall': 0.5804794520547946, 'f1': 0.5880312228967911, 'number': 584}	0.6230	0.6070	0.6149	0.9050
0.0763	18.0	324	0.3559	{'precision': 0.6392294220665499, 'recall': 0.625, 'f1': 0.632034632034632, 'number': 584}	{'precision': 0.5989492119089317, 'recall': 0.5856164383561644, 'f1': 0.5922077922077922, 'number': 584}	0.6191	0.6053	0.6121	0.9075
0.0682	19.0	342	0.3599	{'precision': 0.6666666666666666, 'recall': 0.6335616438356164, 'f1': 0.6496927129060578, 'number': 584}	{'precision': 0.618018018018018, 'recall': 0.5873287671232876, 'f1': 0.6022827041264267, 'number': 584}	0.6423	0.6104	0.6260	0.9086
0.0685	20.0	360	0.3574	{'precision': 0.670863309352518, 'recall': 0.6386986301369864, 'f1': 0.6543859649122807, 'number': 584}	{'precision': 0.6151079136690647, 'recall': 0.5856164383561644, 'f1': 0.6, 'number': 584}	0.6430	0.6122	0.6272	0.9114
0.0591	21.0	378	0.3742	{'precision': 0.6573426573426573, 'recall': 0.6438356164383562, 'f1': 0.6505190311418684, 'number': 584}	{'precision': 0.6171328671328671, 'recall': 0.6044520547945206, 'f1': 0.6107266435986158, 'number': 584}	0.6372	0.6241	0.6306	0.9100
0.0521	22.0	396	0.4063	{'precision': 0.6566901408450704, 'recall': 0.6386986301369864, 'f1': 0.6475694444444444, 'number': 584}	{'precision': 0.6161971830985915, 'recall': 0.5993150684931506, 'f1': 0.607638888888889, 'number': 584}	0.6364	0.6190	0.6276	0.9095
0.0492	23.0	414	0.3971	{'precision': 0.649737302977233, 'recall': 0.6352739726027398, 'f1': 0.6424242424242426, 'number': 584}	{'precision': 0.5971978984238179, 'recall': 0.583904109589041, 'f1': 0.5904761904761905, 'number': 584}	0.6235	0.6096	0.6165	0.9086
0.045	24.0	432	0.4198	{'precision': 0.6448275862068965, 'recall': 0.6404109589041096, 'f1': 0.6426116838487972, 'number': 584}	{'precision': 0.5948275862068966, 'recall': 0.5907534246575342, 'f1': 0.5927835051546393, 'number': 584}	0.6198	0.6156	0.6177	0.9061
0.0391	25.0	450	0.4477	{'precision': 0.643979057591623, 'recall': 0.6318493150684932, 'f1': 0.6378565254969749, 'number': 584}	{'precision': 0.5986038394415357, 'recall': 0.5873287671232876, 'f1': 0.5929127052722557, 'number': 584}	0.6213	0.6096	0.6154	0.9061
0.0411	26.0	468	0.4080	{'precision': 0.6400679117147708, 'recall': 0.6455479452054794, 'f1': 0.6427962489343563, 'number': 584}	{'precision': 0.597623089983022, 'recall': 0.6027397260273972, 'f1': 0.6001705029838021, 'number': 584}	0.6188	0.6241	0.6215	0.9084
0.0369	27.0	486	0.4339	{'precision': 0.6614035087719298, 'recall': 0.6455479452054794, 'f1': 0.6533795493934141, 'number': 584}	{'precision': 0.6105263157894737, 'recall': 0.5958904109589042, 'f1': 0.6031195840554593, 'number': 584}	0.6360	0.6207	0.6282	0.9103
0.0315	28.0	504	0.4303	{'precision': 0.6637931034482759, 'recall': 0.6592465753424658, 'f1': 0.6615120274914089, 'number': 584}	{'precision': 0.6137931034482759, 'recall': 0.6095890410958904, 'f1': 0.6116838487972508, 'number': 584}	0.6388	0.6344	0.6366	0.9117
0.0332	29.0	522	0.4253	{'precision': 0.6643717728055077, 'recall': 0.660958904109589, 'f1': 0.6626609442060085, 'number': 584}	{'precision': 0.6179001721170396, 'recall': 0.6147260273972602, 'f1': 0.6163090128755364, 'number': 584}	0.6411	0.6378	0.6395	0.9134
0.0272	30.0	540	0.4594	{'precision': 0.6495726495726496, 'recall': 0.6506849315068494, 'f1': 0.6501283147989735, 'number': 584}	{'precision': 0.5931623931623932, 'recall': 0.5941780821917808, 'f1': 0.5936698032506416, 'number': 584}	0.6214	0.6224	0.6219	0.9078
0.027	31.0	558	0.4680	{'precision': 0.6621160409556314, 'recall': 0.6643835616438356, 'f1': 0.6632478632478632, 'number': 584}	{'precision': 0.6143344709897611, 'recall': 0.6164383561643836, 'f1': 0.6153846153846154, 'number': 584}	0.6382	0.6404	0.6393	0.9111
0.0295	32.0	576	0.4367	{'precision': 0.6719022687609075, 'recall': 0.6592465753424658, 'f1': 0.6655142610198791, 'number': 584}	{'precision': 0.612565445026178, 'recall': 0.601027397260274, 'f1': 0.6067415730337079, 'number': 584}	0.6422	0.6301	0.6361	0.9120
0.0216	33.0	594	0.4674	{'precision': 0.681260945709282, 'recall': 0.666095890410959, 'f1': 0.6735930735930735, 'number': 584}	{'precision': 0.6357267950963222, 'recall': 0.6215753424657534, 'f1': 0.6285714285714286, 'number': 584}	0.6585	0.6438	0.6511	0.9139
0.0212	34.0	612	0.4702	{'precision': 0.6666666666666666, 'recall': 0.6643835616438356, 'f1': 0.6655231560891938, 'number': 584}	{'precision': 0.6202749140893471, 'recall': 0.6181506849315068, 'f1': 0.6192109777015438, 'number': 584}	0.6435	0.6413	0.6424	0.9103
0.0227	35.0	630	0.4637	{'precision': 0.657672849915683, 'recall': 0.6678082191780822, 'f1': 0.6627017841971112, 'number': 584}	{'precision': 0.6155143338954469, 'recall': 0.625, 'f1': 0.6202209005947323, 'number': 584}	0.6366	0.6464	0.6415	0.9109
0.0196	36.0	648	0.4639	{'precision': 0.6660899653979239, 'recall': 0.6592465753424658, 'f1': 0.6626506024096386, 'number': 584}	{'precision': 0.6141868512110726, 'recall': 0.6078767123287672, 'f1': 0.6110154905335629, 'number': 584}	0.6401	0.6336	0.6368	0.9125
0.0183	37.0	666	0.4656	{'precision': 0.6632478632478632, 'recall': 0.6643835616438356, 'f1': 0.6638152266894781, 'number': 584}	{'precision': 0.6, 'recall': 0.601027397260274, 'f1': 0.6005132591958939, 'number': 584}	0.6316	0.6327	0.6322	0.9131
0.0209	38.0	684	0.4754	{'precision': 0.6649214659685864, 'recall': 0.6523972602739726, 'f1': 0.658599827139153, 'number': 584}	{'precision': 0.6073298429319371, 'recall': 0.5958904109589042, 'f1': 0.6015557476231633, 'number': 584}	0.6361	0.6241	0.6301	0.9131
0.0166	39.0	702	0.4703	{'precision': 0.6695352839931153, 'recall': 0.666095890410959, 'f1': 0.6678111587982833, 'number': 584}	{'precision': 0.612736660929432, 'recall': 0.6095890410958904, 'f1': 0.6111587982832618, 'number': 584}	0.6411	0.6378	0.6395	0.9151
0.0152	40.0	720	0.4739	{'precision': 0.6626712328767124, 'recall': 0.6626712328767124, 'f1': 0.6626712328767124, 'number': 584}	{'precision': 0.6215753424657534, 'recall': 0.6215753424657534, 'f1': 0.6215753424657534, 'number': 584}	0.6421	0.6421	0.6421	0.9139
0.0173	41.0	738	0.4839	{'precision': 0.6610738255033557, 'recall': 0.6746575342465754, 'f1': 0.6677966101694915, 'number': 584}	{'precision': 0.6191275167785235, 'recall': 0.6318493150684932, 'f1': 0.6254237288135593, 'number': 584}	0.6401	0.6533	0.6466	0.9139
0.0162	42.0	756	0.4854	{'precision': 0.6610455311973018, 'recall': 0.6712328767123288, 'f1': 0.6661002548853017, 'number': 584}	{'precision': 0.6138279932546374, 'recall': 0.6232876712328768, 'f1': 0.6185216652506373, 'number': 584}	0.6374	0.6473	0.6423	0.9156
0.0186	43.0	774	0.4747	{'precision': 0.666095890410959, 'recall': 0.666095890410959, 'f1': 0.666095890410959, 'number': 584}	{'precision': 0.6061643835616438, 'recall': 0.6061643835616438, 'f1': 0.6061643835616438, 'number': 584}	0.6361	0.6361	0.6361	0.9156
0.0149	44.0	792	0.4920	{'precision': 0.6695501730103807, 'recall': 0.6626712328767124, 'f1': 0.666092943201377, 'number': 584}	{'precision': 0.6141868512110726, 'recall': 0.6078767123287672, 'f1': 0.6110154905335629, 'number': 584}	0.6419	0.6353	0.6386	0.9139
0.0126	45.0	810	0.4911	{'precision': 0.6621392190152802, 'recall': 0.6678082191780822, 'f1': 0.6649616368286446, 'number': 584}	{'precision': 0.6146010186757216, 'recall': 0.6198630136986302, 'f1': 0.6172208013640239, 'number': 584}	0.6384	0.6438	0.6411	0.9117
0.0142	46.0	828	0.4932	{'precision': 0.671280276816609, 'recall': 0.6643835616438356, 'f1': 0.6678141135972462, 'number': 584}	{'precision': 0.6228373702422145, 'recall': 0.6164383561643836, 'f1': 0.6196213425129088, 'number': 584}	0.6471	0.6404	0.6437	0.9123
0.0107	47.0	846	0.5057	{'precision': 0.6730103806228374, 'recall': 0.666095890410959, 'f1': 0.6695352839931152, 'number': 584}	{'precision': 0.6245674740484429, 'recall': 0.6181506849315068, 'f1': 0.6213425129087781, 'number': 584}	0.6488	0.6421	0.6454	0.9139
0.0127	48.0	864	0.5076	{'precision': 0.6800699300699301, 'recall': 0.666095890410959, 'f1': 0.6730103806228375, 'number': 584}	{'precision': 0.6293706293706294, 'recall': 0.6164383561643836, 'f1': 0.6228373702422145, 'number': 584}	0.6547	0.6413	0.6479	0.9156
0.0116	49.0	882	0.5185	{'precision': 0.6759098786828422, 'recall': 0.6678082191780822, 'f1': 0.6718346253229973, 'number': 584}	{'precision': 0.6291161178509532, 'recall': 0.6215753424657534, 'f1': 0.6253229974160206, 'number': 584}	0.6525	0.6447	0.6486	0.9148
0.0099	50.0	900	0.5142	{'precision': 0.6764705882352942, 'recall': 0.6695205479452054, 'f1': 0.6729776247848538, 'number': 584}	{'precision': 0.629757785467128, 'recall': 0.6232876712328768, 'f1': 0.6265060240963856, 'number': 584}	0.6531	0.6464	0.6497	0.9156

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Jan 9, 2024
Updated: Jan 10, 2024