layoutlm-filtex

elieli/layoutlm-filtex

layoutlm-filtex is a token classification model from elieli. 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: Jul 23, 2024
Updated: Jul 23, 2024
SHA: ed56caf9

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Base models

microsoft/layoutlm-base-uncased

Datasets

funsd

Model card

From the Hugging Face model README

layoutlm-filtex

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: 0.7280
Answer: {'precision': 0.7160087719298246, 'recall': 0.8071693448702101, 'f1': 0.7588611272515979, 'number': 809}
Header: {'precision': 0.33070866141732286, 'recall': 0.35294117647058826, 'f1': 0.34146341463414637, 'number': 119}
Question: {'precision': 0.7805092186128183, 'recall': 0.8347417840375587, 'f1': 0.8067150635208712, 'number': 1065}
Overall Precision: 0.7273
Overall Recall: 0.7948
Overall F1: 0.7595
Overall Accuracy: 0.8050

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8197	1.0	10	1.6109	{'precision': 0.010086455331412104, 'recall': 0.00865265760197775, 'f1': 0.009314703925482368, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.1453125, 'recall': 0.08732394366197183, 'f1': 0.1090909090909091, 'number': 1065}	0.0750	0.0502	0.0601	0.3438
1.4759	2.0	20	1.2685	{'precision': 0.20723684210526316, 'recall': 0.23362175525339926, 'f1': 0.21963974433468914, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.45848915482423336, 'recall': 0.5755868544600939, 'f1': 0.5104079933388842, 'number': 1065}	0.3566	0.4024	0.3781	0.6132
1.1264	3.0	30	0.9432	{'precision': 0.4686046511627907, 'recall': 0.49814585908529047, 'f1': 0.4829239065308568, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6134663341645885, 'recall': 0.6929577464788732, 'f1': 0.6507936507936508, 'number': 1065}	0.5459	0.5725	0.5589	0.7184
0.8604	4.0	40	0.7865	{'precision': 0.6155484558040468, 'recall': 0.7144622991347342, 'f1': 0.6613272311212816, 'number': 809}	{'precision': 0.1590909090909091, 'recall': 0.058823529411764705, 'f1': 0.08588957055214724, 'number': 119}	{'precision': 0.6785079928952042, 'recall': 0.7173708920187793, 'f1': 0.6973984481971702, 'number': 1065}	0.6396	0.6769	0.6577	0.7536
0.6767	5.0	50	0.7196	{'precision': 0.6316916488222698, 'recall': 0.7292954264524104, 'f1': 0.6769936890418818, 'number': 809}	{'precision': 0.19480519480519481, 'recall': 0.12605042016806722, 'f1': 0.15306122448979592, 'number': 119}	{'precision': 0.6669266770670826, 'recall': 0.8028169014084507, 'f1': 0.7285896889646357, 'number': 1065}	0.6367	0.7326	0.6813	0.7738
0.5702	6.0	60	0.6952	{'precision': 0.639, 'recall': 0.7898640296662547, 'f1': 0.7064676616915423, 'number': 809}	{'precision': 0.20454545454545456, 'recall': 0.15126050420168066, 'f1': 0.17391304347826086, 'number': 119}	{'precision': 0.7217915590008613, 'recall': 0.7868544600938967, 'f1': 0.752920035938904, 'number': 1065}	0.6647	0.7501	0.7049	0.7819
0.4943	7.0	70	0.6772	{'precision': 0.6741693461950696, 'recall': 0.7775030902348579, 'f1': 0.722158438576349, 'number': 809}	{'precision': 0.256198347107438, 'recall': 0.2605042016806723, 'f1': 0.25833333333333336, 'number': 119}	{'precision': 0.7392795883361921, 'recall': 0.8093896713615023, 'f1': 0.7727476467951592, 'number': 1065}	0.6856	0.7637	0.7225	0.7892
0.441	8.0	80	0.6809	{'precision': 0.6515151515151515, 'recall': 0.7972805933250927, 'f1': 0.717065036131184, 'number': 809}	{'precision': 0.25225225225225223, 'recall': 0.23529411764705882, 'f1': 0.2434782608695652, 'number': 119}	{'precision': 0.742540494458653, 'recall': 0.8178403755868544, 'f1': 0.778373547810545, 'number': 1065}	0.6790	0.7747	0.7237	0.7882
0.3859	9.0	90	0.6842	{'precision': 0.6882416396979504, 'recall': 0.788627935723115, 'f1': 0.7350230414746545, 'number': 809}	{'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119}	{'precision': 0.7545064377682403, 'recall': 0.8253521126760563, 'f1': 0.788340807174888, 'number': 1065}	0.7010	0.7787	0.7378	0.7950
0.3813	10.0	100	0.6972	{'precision': 0.6871686108165429, 'recall': 0.8009888751545118, 'f1': 0.7397260273972602, 'number': 809}	{'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}	{'precision': 0.7651646447140381, 'recall': 0.8291079812206573, 'f1': 0.7958539882830105, 'number': 1065}	0.7100	0.7873	0.7466	0.8040
0.3202	11.0	110	0.7074	{'precision': 0.7015250544662309, 'recall': 0.796044499381953, 'f1': 0.7458019687319051, 'number': 809}	{'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119}	{'precision': 0.7665805340223945, 'recall': 0.8356807511737089, 'f1': 0.7996406109613656, 'number': 1065}	0.7143	0.7903	0.7504	0.7938
0.3009	12.0	120	0.7242	{'precision': 0.7027027027027027, 'recall': 0.8034610630407911, 'f1': 0.7497116493656287, 'number': 809}	{'precision': 0.32786885245901637, 'recall': 0.33613445378151263, 'f1': 0.33195020746887965, 'number': 119}	{'precision': 0.7851387645478961, 'recall': 0.8234741784037559, 'f1': 0.8038496791934006, 'number': 1065}	0.7241	0.7863	0.7539	0.8031
0.2915	13.0	130	0.7256	{'precision': 0.705945945945946, 'recall': 0.8071693448702101, 'f1': 0.7531718569780853, 'number': 809}	{'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119}	{'precision': 0.7808939526730938, 'recall': 0.8366197183098592, 'f1': 0.8077969174977334, 'number': 1065}	0.7237	0.7953	0.7578	0.8024
0.2644	14.0	140	0.7278	{'precision': 0.712882096069869, 'recall': 0.8071693448702101, 'f1': 0.7571014492753623, 'number': 809}	{'precision': 0.3282442748091603, 'recall': 0.36134453781512604, 'f1': 0.344, 'number': 119}	{'precision': 0.7841409691629956, 'recall': 0.8356807511737089, 'f1': 0.8090909090909091, 'number': 1065}	0.7269	0.7958	0.7598	0.8034
0.2654	15.0	150	0.7280	{'precision': 0.7160087719298246, 'recall': 0.8071693448702101, 'f1': 0.7588611272515979, 'number': 809}	{'precision': 0.33070866141732286, 'recall': 0.35294117647058826, 'f1': 0.34146341463414637, 'number': 119}	{'precision': 0.7805092186128183, 'recall': 0.8347417840375587, 'f1': 0.8067150635208712, 'number': 1065}	0.7273	0.7948	0.7595	0.8050

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

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