layoutlm-funsd

riasharma/layoutlm-funsd

layoutlm-funsd is a token classification model from riasharma. 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: Dec 26, 2023
Updated: Dec 28, 2023
SHA: 5d1ba102

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Base models

microsoft/layoutlm-base-uncased

Model card

From the Hugging Face model README

layoutlm-funsd

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

Loss: 0.7207
Answer: {'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809}
Header: {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}
Question: {'precision': 0.7707061900610288, 'recall': 0.8300469483568075, 'f1': 0.7992766726943942, 'number': 1065}
Overall Precision: 0.7203
Overall Recall: 0.7883
Overall F1: 0.7528
Overall Accuracy: 0.7971

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
1.8417	1.0	10	1.6166	{'precision': 0.028741328047571853, 'recall': 0.03584672435105068, 'f1': 0.0319031903190319, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.16827852998065765, 'recall': 0.16338028169014085, 'f1': 0.16579323487374942, 'number': 1065}	0.0993	0.1019	0.1006	0.3810
1.4476	2.0	20	1.2599	{'precision': 0.15711947626841244, 'recall': 0.11866501854140915, 'f1': 0.13521126760563382, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.46172441579371476, 'recall': 0.5380281690140845, 'f1': 0.4969644405897658, 'number': 1065}	0.3612	0.3357	0.3480	0.5747
1.1227	3.0	30	0.9780	{'precision': 0.4661214953271028, 'recall': 0.4932014833127318, 'f1': 0.4792792792792793, 'number': 809}	{'precision': 0.16279069767441862, 'recall': 0.058823529411764705, 'f1': 0.08641975308641975, 'number': 119}	{'precision': 0.5979020979020979, 'recall': 0.6422535211267606, 'f1': 0.6192847442281576, 'number': 1065}	0.5335	0.5469	0.5401	0.7036
0.8596	4.0	40	0.7946	{'precision': 0.5789473684210527, 'recall': 0.6526576019777504, 'f1': 0.6135967460778617, 'number': 809}	{'precision': 0.24193548387096775, 'recall': 0.12605042016806722, 'f1': 0.16574585635359115, 'number': 119}	{'precision': 0.6658141517476556, 'recall': 0.7333333333333333, 'f1': 0.6979445933869526, 'number': 1065}	0.6167	0.6643	0.6396	0.7589
0.6705	5.0	50	0.7132	{'precision': 0.6424759871931697, 'recall': 0.7441285537700866, 'f1': 0.6895761741122567, 'number': 809}	{'precision': 0.3333333333333333, 'recall': 0.21008403361344538, 'f1': 0.2577319587628866, 'number': 119}	{'precision': 0.6747376916868443, 'recall': 0.7849765258215963, 'f1': 0.7256944444444444, 'number': 1065}	0.6499	0.7341	0.6894	0.7767
0.5653	6.0	60	0.6840	{'precision': 0.653125, 'recall': 0.7750309023485785, 'f1': 0.7088750706613907, 'number': 809}	{'precision': 0.30952380952380953, 'recall': 0.2184873949579832, 'f1': 0.2561576354679803, 'number': 119}	{'precision': 0.7077814569536424, 'recall': 0.8028169014084507, 'f1': 0.7523097228332599, 'number': 1065}	0.6696	0.7566	0.7105	0.7846
0.4959	7.0	70	0.6684	{'precision': 0.6872964169381107, 'recall': 0.7824474660074165, 'f1': 0.7317919075144509, 'number': 809}	{'precision': 0.2815533980582524, 'recall': 0.24369747899159663, 'f1': 0.26126126126126126, 'number': 119}	{'precision': 0.734006734006734, 'recall': 0.8187793427230047, 'f1': 0.7740790057700843, 'number': 1065}	0.6935	0.7697	0.7296	0.7950
0.4343	8.0	80	0.6696	{'precision': 0.6898395721925134, 'recall': 0.7972805933250927, 'f1': 0.7396788990825688, 'number': 809}	{'precision': 0.26956521739130435, 'recall': 0.2605042016806723, 'f1': 0.264957264957265, 'number': 119}	{'precision': 0.7495769881556683, 'recall': 0.831924882629108, 'f1': 0.7886070315976857, 'number': 1065}	0.6998	0.7837	0.7394	0.7987
0.375	9.0	90	0.6760	{'precision': 0.7105549510337323, 'recall': 0.8071693448702101, 'f1': 0.755787037037037, 'number': 809}	{'precision': 0.25, 'recall': 0.2605042016806723, 'f1': 0.25514403292181076, 'number': 119}	{'precision': 0.7758007117437722, 'recall': 0.8187793427230047, 'f1': 0.7967108268615805, 'number': 1065}	0.7180	0.7807	0.7481	0.7992
0.3532	10.0	100	0.6802	{'precision': 0.7147470398277718, 'recall': 0.8207663782447466, 'f1': 0.7640966628308401, 'number': 809}	{'precision': 0.3018867924528302, 'recall': 0.2689075630252101, 'f1': 0.28444444444444444, 'number': 119}	{'precision': 0.7761061946902655, 'recall': 0.8234741784037559, 'f1': 0.7990888382687927, 'number': 1065}	0.7266	0.7893	0.7566	0.8046
0.3265	11.0	110	0.6995	{'precision': 0.6968716289104638, 'recall': 0.7985166872682324, 'f1': 0.7442396313364056, 'number': 809}	{'precision': 0.308411214953271, 'recall': 0.2773109243697479, 'f1': 0.29203539823008845, 'number': 119}	{'precision': 0.7589134125636672, 'recall': 0.8394366197183099, 'f1': 0.7971466785555059, 'number': 1065}	0.7111	0.7893	0.7482	0.7973
0.3023	12.0	120	0.7053	{'precision': 0.7027896995708155, 'recall': 0.8096415327564895, 'f1': 0.7524411257897761, 'number': 809}	{'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119}	{'precision': 0.769434628975265, 'recall': 0.8178403755868544, 'f1': 0.7928994082840236, 'number': 1065}	0.7131	0.7858	0.7477	0.7991
0.2927	13.0	130	0.7080	{'precision': 0.7024704618689581, 'recall': 0.8084054388133498, 'f1': 0.7517241379310345, 'number': 809}	{'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119}	{'precision': 0.7679033649698016, 'recall': 0.8356807511737089, 'f1': 0.8003597122302158, 'number': 1065}	0.7171	0.7923	0.7528	0.7999
0.2756	14.0	140	0.7128	{'precision': 0.7081967213114754, 'recall': 0.8009888751545118, 'f1': 0.7517401392111368, 'number': 809}	{'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119}	{'precision': 0.7720524017467248, 'recall': 0.8300469483568075, 'f1': 0.7999999999999999, 'number': 1065}	0.7219	0.7868	0.7529	0.7984
0.2741	15.0	150	0.7207	{'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809}	{'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}	{'precision': 0.7707061900610288, 'recall': 0.8300469483568075, 'f1': 0.7992766726943942, 'number': 1065}	0.7203	0.7883	0.7528	0.7971

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Dec 26, 2023
Updated: Dec 28, 2023