layoutlm-funsd

JawalHingrajia/layoutlm-funsd

layoutlm-funsd is a machine learning model from JawalHingrajia. 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 8, 2024
Updated: Aug 9, 2024
SHA: 459e1e49

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: 0.6811
Answer: {'precision': 0.709070796460177, 'recall': 0.792336217552534, 'f1': 0.7483946293053124, 'number': 809}
Header: {'precision': 0.30935251798561153, 'recall': 0.36134453781512604, 'f1': 0.33333333333333337, 'number': 119}
Question: {'precision': 0.7719298245614035, 'recall': 0.8262910798122066, 'f1': 0.7981859410430839, 'number': 1065}
Overall Precision: 0.7164
Overall Recall: 0.7847
Overall F1: 0.7490
Overall Accuracy: 0.8082

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
1.7509	1.0	10	1.5595	{'precision': 0.017814726840855107, 'recall': 0.018541409147095178, 'f1': 0.018170805572380374, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2612244897959184, 'recall': 0.18028169014084508, 'f1': 0.21333333333333337, 'number': 1065}	0.1313	0.1039	0.1160	0.3731
1.4219	2.0	20	1.2145	{'precision': 0.2700871248789932, 'recall': 0.34487021013597036, 'f1': 0.30293159609120524, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.44834710743801653, 'recall': 0.6112676056338028, 'f1': 0.5172824791418356, 'number': 1065}	0.3742	0.4666	0.4154	0.6174
1.051	3.0	30	0.8818	{'precision': 0.5245579567779961, 'recall': 0.6600741656365884, 'f1': 0.5845648604269295, 'number': 809}	{'precision': 0.08823529411764706, 'recall': 0.025210084033613446, 'f1': 0.039215686274509796, 'number': 119}	{'precision': 0.5788679245283019, 'recall': 0.72018779342723, 'f1': 0.6418410041841004, 'number': 1065}	0.5486	0.6543	0.5968	0.7169
0.8148	4.0	40	0.7627	{'precision': 0.5905587668593449, 'recall': 0.757725587144623, 'f1': 0.6637791012452626, 'number': 809}	{'precision': 0.16981132075471697, 'recall': 0.07563025210084033, 'f1': 0.10465116279069768, 'number': 119}	{'precision': 0.6629502572898799, 'recall': 0.7258215962441315, 'f1': 0.6929627969520394, 'number': 1065}	0.6181	0.6999	0.6565	0.7571
0.655	5.0	50	0.7092	{'precision': 0.6670305676855895, 'recall': 0.7552533992583437, 'f1': 0.7084057971014494, 'number': 809}	{'precision': 0.20652173913043478, 'recall': 0.15966386554621848, 'f1': 0.1800947867298578, 'number': 119}	{'precision': 0.6726986624704957, 'recall': 0.8028169014084507, 'f1': 0.7320205479452055, 'number': 1065}	0.6516	0.7451	0.6952	0.7782
0.5597	6.0	60	0.6707	{'precision': 0.6635220125786163, 'recall': 0.7824474660074165, 'f1': 0.7180941576857629, 'number': 809}	{'precision': 0.24242424242424243, 'recall': 0.20168067226890757, 'f1': 0.2201834862385321, 'number': 119}	{'precision': 0.7158608990670059, 'recall': 0.7924882629107981, 'f1': 0.7522281639928697, 'number': 1065}	0.6725	0.7531	0.7105	0.7910
0.4852	7.0	70	0.6426	{'precision': 0.6834763948497854, 'recall': 0.7873918417799752, 'f1': 0.7317633543940264, 'number': 809}	{'precision': 0.27927927927927926, 'recall': 0.2605042016806723, 'f1': 0.26956521739130435, 'number': 119}	{'precision': 0.7425658453695837, 'recall': 0.8206572769953052, 'f1': 0.7796610169491525, 'number': 1065}	0.6946	0.7737	0.7320	0.8031
0.4304	8.0	80	0.6514	{'precision': 0.6897654584221748, 'recall': 0.799752781211372, 'f1': 0.7406983400114482, 'number': 809}	{'precision': 0.24285714285714285, 'recall': 0.2857142857142857, 'f1': 0.2625482625482625, 'number': 119}	{'precision': 0.7366638441998307, 'recall': 0.8169014084507042, 'f1': 0.7747105966162066, 'number': 1065}	0.6866	0.7782	0.7295	0.7993
0.3848	9.0	90	0.6446	{'precision': 0.7036625971143174, 'recall': 0.7836835599505563, 'f1': 0.7415204678362575, 'number': 809}	{'precision': 0.2677165354330709, 'recall': 0.2857142857142857, 'f1': 0.2764227642276422, 'number': 119}	{'precision': 0.7435456110154905, 'recall': 0.8112676056338028, 'f1': 0.7759317467444994, 'number': 1065}	0.6995	0.7687	0.7325	0.8035
0.3788	10.0	100	0.6471	{'precision': 0.7045454545454546, 'recall': 0.8046971569839307, 'f1': 0.7512983266012695, 'number': 809}	{'precision': 0.26717557251908397, 'recall': 0.29411764705882354, 'f1': 0.28, 'number': 119}	{'precision': 0.7420435510887772, 'recall': 0.831924882629108, 'f1': 0.7844178840194777, 'number': 1065}	0.6990	0.7888	0.7412	0.8082
0.3236	11.0	110	0.6526	{'precision': 0.7038251366120218, 'recall': 0.796044499381953, 'f1': 0.7470997679814385, 'number': 809}	{'precision': 0.29850746268656714, 'recall': 0.33613445378151263, 'f1': 0.31620553359683795, 'number': 119}	{'precision': 0.7566638005159071, 'recall': 0.8262910798122066, 'f1': 0.7899461400359066, 'number': 1065}	0.7071	0.7847	0.7439	0.8054
0.3053	12.0	120	0.6676	{'precision': 0.7138047138047138, 'recall': 0.7861557478368356, 'f1': 0.748235294117647, 'number': 809}	{'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}	{'precision': 0.7703768624014022, 'recall': 0.8253521126760563, 'f1': 0.7969174977334541, 'number': 1065}	0.7199	0.7802	0.7489	0.8098
0.2881	13.0	130	0.6718	{'precision': 0.7058177826564215, 'recall': 0.7948084054388134, 'f1': 0.7476744186046511, 'number': 809}	{'precision': 0.312, 'recall': 0.3277310924369748, 'f1': 0.31967213114754095, 'number': 119}	{'precision': 0.7652173913043478, 'recall': 0.8262910798122066, 'f1': 0.7945823927765238, 'number': 1065}	0.7145	0.7837	0.7475	0.8073
0.2668	14.0	140	0.6787	{'precision': 0.7093922651933702, 'recall': 0.7935723114956736, 'f1': 0.7491248541423571, 'number': 809}	{'precision': 0.30714285714285716, 'recall': 0.36134453781512604, 'f1': 0.33204633204633205, 'number': 119}	{'precision': 0.7713787085514834, 'recall': 0.8300469483568075, 'f1': 0.7996381727725013, 'number': 1065}	0.7161	0.7873	0.7500	0.8081
0.2692	15.0	150	0.6811	{'precision': 0.709070796460177, 'recall': 0.792336217552534, 'f1': 0.7483946293053124, 'number': 809}	{'precision': 0.30935251798561153, 'recall': 0.36134453781512604, 'f1': 0.33333333333333337, 'number': 119}	{'precision': 0.7719298245614035, 'recall': 0.8262910798122066, 'f1': 0.7981859410430839, 'number': 1065}	0.7164	0.7847	0.7490	0.8082

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Type: layoutlm
License: mit
Created: Aug 8, 2024
Updated: Aug 9, 2024