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