layoutlm-funsd

asharma06/layoutlm-funsd

layoutlm-funsd is a token classification model from asharma06. 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: Jun 22, 2024
Updated: Jun 23, 2024
SHA: f80f6e46

Try a prompt

Base models

microsoft/layoutlm-base-uncased

Datasets

funsd

Model card

From the Hugging Face model README

layoutlm-funsd

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

Loss: 0.8048
Answer: {'precision': 0.7424412094064949, 'recall': 0.8195302843016069, 'f1': 0.7790834312573444, 'number': 809}
Header: {'precision': 0.41304347826086957, 'recall': 0.4789915966386555, 'f1': 0.443579766536965, 'number': 119}
Question: {'precision': 0.8048561151079137, 'recall': 0.8403755868544601, 'f1': 0.8222324299494717, 'number': 1065}
Overall Precision: 0.7536
Overall Recall: 0.8103
Overall F1: 0.7809
Overall Accuracy: 0.8256

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: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8389	1.0	10	1.6291	{'precision': 0.01568627450980392, 'recall': 0.009888751545117428, 'f1': 0.012130401819560273, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2798165137614679, 'recall': 0.11455399061032864, 'f1': 0.16255829447035308, 'number': 1065}	0.1374	0.0652	0.0885	0.3319
1.4797	2.0	20	1.2835	{'precision': 0.2250740375123396, 'recall': 0.28182941903584674, 'f1': 0.2502744237102085, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3997214484679666, 'recall': 0.5389671361502347, 'f1': 0.45901639344262296, 'number': 1065}	0.3275	0.4024	0.3611	0.5792
1.1281	3.0	30	0.9324	{'precision': 0.47114375655823715, 'recall': 0.5550061804697157, 'f1': 0.5096481271282634, 'number': 809}	{'precision': 0.06060606060606061, 'recall': 0.01680672268907563, 'f1': 0.02631578947368421, 'number': 119}	{'precision': 0.5470149253731343, 'recall': 0.6882629107981221, 'f1': 0.6095634095634096, 'number': 1065}	0.5090	0.5941	0.5483	0.7008
0.848	4.0	40	0.7620	{'precision': 0.5925563173359452, 'recall': 0.7478368355995055, 'f1': 0.6612021857923498, 'number': 809}	{'precision': 0.17391304347826086, 'recall': 0.10084033613445378, 'f1': 0.12765957446808512, 'number': 119}	{'precision': 0.6578293289146645, 'recall': 0.7455399061032864, 'f1': 0.6989436619718309, 'number': 1065}	0.6143	0.7080	0.6578	0.7624
0.6618	5.0	50	0.6889	{'precision': 0.6424180327868853, 'recall': 0.7750309023485785, 'f1': 0.7025210084033613, 'number': 809}	{'precision': 0.3, 'recall': 0.226890756302521, 'f1': 0.25837320574162675, 'number': 119}	{'precision': 0.6919967663702506, 'recall': 0.8037558685446009, 'f1': 0.7437011294526499, 'number': 1065}	0.6557	0.7577	0.7030	0.7901
0.5475	6.0	60	0.6690	{'precision': 0.654158215010142, 'recall': 0.7972805933250927, 'f1': 0.7186629526462396, 'number': 809}	{'precision': 0.31868131868131866, 'recall': 0.24369747899159663, 'f1': 0.2761904761904762, 'number': 119}	{'precision': 0.7417102966841187, 'recall': 0.7981220657276995, 'f1': 0.7688828584350972, 'number': 1065}	0.6856	0.7647	0.7230	0.7949
0.4641	7.0	70	0.6472	{'precision': 0.6896551724137931, 'recall': 0.7911001236093943, 'f1': 0.7369027058146229, 'number': 809}	{'precision': 0.23529411764705882, 'recall': 0.23529411764705882, 'f1': 0.23529411764705882, 'number': 119}	{'precision': 0.7485131690739167, 'recall': 0.8272300469483568, 'f1': 0.7859054415700267, 'number': 1065}	0.6965	0.7772	0.7346	0.8108
0.3968	8.0	80	0.6603	{'precision': 0.7052518756698821, 'recall': 0.8133498145859085, 'f1': 0.7554535017221584, 'number': 809}	{'precision': 0.26277372262773724, 'recall': 0.3025210084033613, 'f1': 0.28125000000000006, 'number': 119}	{'precision': 0.7734513274336283, 'recall': 0.8206572769953052, 'f1': 0.7963553530751709, 'number': 1065}	0.7127	0.7868	0.7479	0.8117
0.3377	9.0	90	0.6641	{'precision': 0.7273730684326711, 'recall': 0.8145859085290482, 'f1': 0.7685131195335277, 'number': 809}	{'precision': 0.30612244897959184, 'recall': 0.37815126050420167, 'f1': 0.3383458646616541, 'number': 119}	{'precision': 0.7655838454784899, 'recall': 0.8187793427230047, 'f1': 0.7912885662431942, 'number': 1065}	0.7190	0.7908	0.7532	0.8063
0.3159	10.0	100	0.6626	{'precision': 0.7112299465240641, 'recall': 0.8220024721878862, 'f1': 0.7626146788990825, 'number': 809}	{'precision': 0.36666666666666664, 'recall': 0.2773109243697479, 'f1': 0.31578947368421056, 'number': 119}	{'precision': 0.7945945945945946, 'recall': 0.828169014084507, 'f1': 0.8110344827586206, 'number': 1065}	0.7400	0.7928	0.7655	0.8252
0.2565	11.0	110	0.6831	{'precision': 0.706951871657754, 'recall': 0.8170580964153276, 'f1': 0.7580275229357798, 'number': 809}	{'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}	{'precision': 0.7935656836461126, 'recall': 0.8338028169014085, 'f1': 0.8131868131868133, 'number': 1065}	0.7319	0.7973	0.7632	0.8146
0.2326	12.0	120	0.7081	{'precision': 0.7152103559870551, 'recall': 0.8195302843016069, 'f1': 0.7638248847926269, 'number': 809}	{'precision': 0.34375, 'recall': 0.3697478991596639, 'f1': 0.3562753036437247, 'number': 119}	{'precision': 0.7731601731601732, 'recall': 0.8384976525821596, 'f1': 0.8045045045045045, 'number': 1065}	0.7240	0.8028	0.7614	0.8097
0.2064	13.0	130	0.7088	{'precision': 0.7420454545454546, 'recall': 0.8071693448702101, 'f1': 0.773238602723505, 'number': 809}	{'precision': 0.375, 'recall': 0.37815126050420167, 'f1': 0.37656903765690375, 'number': 119}	{'precision': 0.7978628673196795, 'recall': 0.8413145539906103, 'f1': 0.8190127970749542, 'number': 1065}	0.7508	0.7998	0.7745	0.8216
0.1807	14.0	140	0.7149	{'precision': 0.7113289760348583, 'recall': 0.8071693448702101, 'f1': 0.7562246670526924, 'number': 809}	{'precision': 0.373134328358209, 'recall': 0.42016806722689076, 'f1': 0.3952569169960475, 'number': 119}	{'precision': 0.8001800180018002, 'recall': 0.8347417840375587, 'f1': 0.8170955882352942, 'number': 1065}	0.7360	0.7988	0.7661	0.8186
0.1673	15.0	150	0.7429	{'precision': 0.7461988304093568, 'recall': 0.788627935723115, 'f1': 0.766826923076923, 'number': 809}	{'precision': 0.4015151515151515, 'recall': 0.44537815126050423, 'f1': 0.4223107569721115, 'number': 119}	{'precision': 0.8001800180018002, 'recall': 0.8347417840375587, 'f1': 0.8170955882352942, 'number': 1065}	0.7531	0.7928	0.7724	0.8213
0.158	16.0	160	0.7579	{'precision': 0.7352614015572859, 'recall': 0.8170580964153276, 'f1': 0.7740046838407495, 'number': 809}	{'precision': 0.3673469387755102, 'recall': 0.453781512605042, 'f1': 0.406015037593985, 'number': 119}	{'precision': 0.790616854908775, 'recall': 0.8544600938967136, 'f1': 0.8212996389891697, 'number': 1065}	0.7396	0.8154	0.7757	0.8166
0.1407	17.0	170	0.7595	{'precision': 0.7474747474747475, 'recall': 0.823238566131026, 'f1': 0.783529411764706, 'number': 809}	{'precision': 0.424, 'recall': 0.44537815126050423, 'f1': 0.4344262295081967, 'number': 119}	{'precision': 0.8081081081081081, 'recall': 0.8422535211267606, 'f1': 0.8248275862068966, 'number': 1065}	0.7601	0.8108	0.7847	0.8237
0.1277	18.0	180	0.7927	{'precision': 0.7305986696230599, 'recall': 0.8145859085290482, 'f1': 0.7703097603740503, 'number': 809}	{'precision': 0.4140625, 'recall': 0.44537815126050423, 'f1': 0.42914979757085026, 'number': 119}	{'precision': 0.8114233907524931, 'recall': 0.8403755868544601, 'f1': 0.8256457564575646, 'number': 1065}	0.7534	0.8063	0.7790	0.8137
0.1268	19.0	190	0.7819	{'precision': 0.7361894024802705, 'recall': 0.8071693448702101, 'f1': 0.7700471698113207, 'number': 809}	{'precision': 0.4330708661417323, 'recall': 0.46218487394957986, 'f1': 0.4471544715447155, 'number': 119}	{'precision': 0.8028419182948491, 'recall': 0.8488262910798122, 'f1': 0.8251939753537197, 'number': 1065}	0.7533	0.8088	0.7801	0.8216
0.1112	20.0	200	0.7880	{'precision': 0.740782122905028, 'recall': 0.8195302843016069, 'f1': 0.7781690140845071, 'number': 809}	{'precision': 0.4195804195804196, 'recall': 0.5042016806722689, 'f1': 0.4580152671755725, 'number': 119}	{'precision': 0.8075880758807588, 'recall': 0.8394366197183099, 'f1': 0.8232044198895027, 'number': 1065}	0.7538	0.8113	0.7815	0.8229
0.1096	21.0	210	0.7925	{'precision': 0.7404494382022472, 'recall': 0.8145859085290482, 'f1': 0.7757504414361388, 'number': 809}	{'precision': 0.45454545454545453, 'recall': 0.42016806722689076, 'f1': 0.43668122270742354, 'number': 119}	{'precision': 0.815049864007253, 'recall': 0.844131455399061, 'f1': 0.8293357933579335, 'number': 1065}	0.7646	0.8068	0.7852	0.8249
0.1158	22.0	220	0.8093	{'precision': 0.7363128491620111, 'recall': 0.8145859085290482, 'f1': 0.7734741784037558, 'number': 809}	{'precision': 0.41333333333333333, 'recall': 0.5210084033613446, 'f1': 0.4609665427509294, 'number': 119}	{'precision': 0.8030438675022381, 'recall': 0.8422535211267606, 'f1': 0.8221814848762603, 'number': 1065}	0.7484	0.8118	0.7788	0.8210
0.0985	23.0	230	0.8013	{'precision': 0.7554535017221584, 'recall': 0.8133498145859085, 'f1': 0.7833333333333333, 'number': 809}	{'precision': 0.45689655172413796, 'recall': 0.44537815126050423, 'f1': 0.4510638297872341, 'number': 119}	{'precision': 0.8091809180918091, 'recall': 0.844131455399061, 'f1': 0.8262867647058824, 'number': 1065}	0.7674	0.8078	0.7871	0.8279
0.0988	24.0	240	0.8040	{'precision': 0.7385984427141268, 'recall': 0.8207663782447466, 'f1': 0.7775175644028104, 'number': 809}	{'precision': 0.4198473282442748, 'recall': 0.46218487394957986, 'f1': 0.43999999999999995, 'number': 119}	{'precision': 0.8016157989228008, 'recall': 0.8384976525821596, 'f1': 0.8196420376319412, 'number': 1065}	0.7519	0.8088	0.7793	0.8255
0.1004	25.0	250	0.8048	{'precision': 0.7424412094064949, 'recall': 0.8195302843016069, 'f1': 0.7790834312573444, 'number': 809}	{'precision': 0.41304347826086957, 'recall': 0.4789915966386555, 'f1': 0.443579766536965, 'number': 119}	{'precision': 0.8048561151079137, 'recall': 0.8403755868544601, 'f1': 0.8222324299494717, 'number': 1065}	0.7536	0.8103	0.7809	0.8256

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Jun 22, 2024
Updated: Jun 23, 2024