layoutlm-funsd

KushalBanda/layoutlm-funsd

layoutlm-funsd is a machine learning model from KushalBanda. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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

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At a glance

License: mit
Model type: layoutlm
Access: Public
Created: Aug 12, 2024
Updated: Aug 14, 2024
SHA: 3d6f7420

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: 0.6932
Answer: {'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809}
Header: {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}
Question: {'precision': 0.766107678729038, 'recall': 0.8150234741784037, 'f1': 0.7898089171974523, 'number': 1065}
Overall Precision: 0.7093
Overall Recall: 0.7822
Overall F1: 0.7440
Overall Accuracy: 0.8018

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
1.8301	1.0	10	1.5866	{'precision': 0.006765899864682003, 'recall': 0.006180469715698393, 'f1': 0.006459948320413437, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2246153846153846, 'recall': 0.13708920187793427, 'f1': 0.17026239067055393, 'number': 1065}	0.1087	0.0758	0.0893	0.3526
1.4768	2.0	20	1.2757	{'precision': 0.280557834290402, 'recall': 0.4227441285537701, 'f1': 0.3372781065088757, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3888491779842745, 'recall': 0.5107981220657277, 'f1': 0.44155844155844154, 'number': 1065}	0.3380	0.4446	0.3840	0.6011
1.1406	3.0	30	0.9524	{'precision': 0.46350710900473935, 'recall': 0.6044499381953028, 'f1': 0.5246781115879828, 'number': 809}	{'precision': 0.06382978723404255, 'recall': 0.025210084033613446, 'f1': 0.03614457831325301, 'number': 119}	{'precision': 0.53671875, 'recall': 0.6450704225352113, 'f1': 0.5859275053304905, 'number': 1065}	0.4950	0.5916	0.5390	0.6937
0.8606	4.0	40	0.7865	{'precision': 0.5620437956204379, 'recall': 0.761433868974042, 'f1': 0.6467191601049869, 'number': 809}	{'precision': 0.16666666666666666, 'recall': 0.10084033613445378, 'f1': 0.1256544502617801, 'number': 119}	{'precision': 0.6464285714285715, 'recall': 0.67981220657277, 'f1': 0.662700228832952, 'number': 1065}	0.5909	0.6784	0.6316	0.7552
0.6873	5.0	50	0.7157	{'precision': 0.6341719077568134, 'recall': 0.7478368355995055, 'f1': 0.6863301191151445, 'number': 809}	{'precision': 0.375, 'recall': 0.25210084033613445, 'f1': 0.3015075376884422, 'number': 119}	{'precision': 0.6704730831973899, 'recall': 0.7718309859154929, 'f1': 0.7175905718027062, 'number': 1065}	0.6447	0.7311	0.6852	0.7767
0.5888	6.0	60	0.6909	{'precision': 0.6243949661181026, 'recall': 0.7972805933250927, 'f1': 0.7003257328990228, 'number': 809}	{'precision': 0.35064935064935066, 'recall': 0.226890756302521, 'f1': 0.2755102040816326, 'number': 119}	{'precision': 0.7193923145665773, 'recall': 0.755868544600939, 'f1': 0.7371794871794871, 'number': 1065}	0.6626	0.7411	0.6997	0.7806
0.5097	7.0	70	0.6576	{'precision': 0.6656050955414012, 'recall': 0.7750309023485785, 'f1': 0.7161621930325527, 'number': 809}	{'precision': 0.32323232323232326, 'recall': 0.2689075630252101, 'f1': 0.29357798165137616, 'number': 119}	{'precision': 0.7382198952879581, 'recall': 0.7943661971830986, 'f1': 0.7652645861601085, 'number': 1065}	0.6882	0.7551	0.7201	0.7963
0.4507	8.0	80	0.6668	{'precision': 0.6615698267074414, 'recall': 0.8022249690976514, 'f1': 0.7251396648044692, 'number': 809}	{'precision': 0.28205128205128205, 'recall': 0.2773109243697479, 'f1': 0.2796610169491525, 'number': 119}	{'precision': 0.7389380530973452, 'recall': 0.784037558685446, 'f1': 0.7608200455580865, 'number': 1065}	0.6809	0.7612	0.7188	0.7909
0.3998	9.0	90	0.6639	{'precision': 0.6715481171548117, 'recall': 0.7935723114956736, 'f1': 0.7274787535410764, 'number': 809}	{'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119}	{'precision': 0.7542448614834674, 'recall': 0.7924882629107981, 'f1': 0.7728937728937729, 'number': 1065}	0.6950	0.7637	0.7277	0.7942
0.3899	10.0	100	0.6686	{'precision': 0.6840981856990395, 'recall': 0.792336217552534, 'f1': 0.734249713631157, 'number': 809}	{'precision': 0.31092436974789917, 'recall': 0.31092436974789917, 'f1': 0.31092436974789917, 'number': 119}	{'precision': 0.752828546562228, 'recall': 0.812206572769953, 'f1': 0.7813911472448057, 'number': 1065}	0.6998	0.7742	0.7351	0.7987
0.3345	11.0	110	0.6688	{'precision': 0.6878980891719745, 'recall': 0.8009888751545118, 'f1': 0.7401484865790977, 'number': 809}	{'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119}	{'precision': 0.7567332754126846, 'recall': 0.8178403755868544, 'f1': 0.7861010830324908, 'number': 1065}	0.7028	0.7817	0.7401	0.8019
0.3227	12.0	120	0.6747	{'precision': 0.6944444444444444, 'recall': 0.8034610630407911, 'f1': 0.7449856733524356, 'number': 809}	{'precision': 0.35714285714285715, 'recall': 0.33613445378151263, 'f1': 0.34632034632034636, 'number': 119}	{'precision': 0.7703306523681859, 'recall': 0.8093896713615023, 'f1': 0.7893772893772893, 'number': 1065}	0.7162	0.7787	0.7462	0.8047
0.3068	13.0	130	0.6875	{'precision': 0.6957470010905126, 'recall': 0.788627935723115, 'f1': 0.7392815758980301, 'number': 809}	{'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119}	{'precision': 0.7596899224806202, 'recall': 0.828169014084507, 'f1': 0.7924528301886793, 'number': 1065}	0.7083	0.7832	0.7439	0.8024
0.2826	14.0	140	0.6897	{'precision': 0.6963519313304721, 'recall': 0.8022249690976514, 'f1': 0.7455485353245261, 'number': 809}	{'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}	{'precision': 0.7651183172655566, 'recall': 0.819718309859155, 'f1': 0.7914777878513146, 'number': 1065}	0.7113	0.7837	0.7458	0.8007
0.2785	15.0	150	0.6932	{'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809}	{'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}	{'precision': 0.766107678729038, 'recall': 0.8150234741784037, 'f1': 0.7898089171974523, 'number': 1065}	0.7093	0.7822	0.7440	0.8018

Framework versions

Transformers 4.44.0
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Type: layoutlm
License: mit
Created: Aug 12, 2024
Updated: Aug 14, 2024