layoutlm-funsd-pytorch

jinhybr/layoutlm-funsd-pytorch

layoutlm-funsd-pytorch is a token classification model from jinhybr. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Oct 21, 2022
Updated: Oct 21, 2022
SHA: 33a82753

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Datasets

funsd

Model card

From the Hugging Face model README

layoutlm-funsd-pytorch

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: 0.7042
Answer: {'precision': 0.712403951701427, 'recall': 0.8022249690976514, 'f1': 0.7546511627906977, 'number': 809}
Header: {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119}
Question: {'precision': 0.7747589833479404, 'recall': 0.8300469483568075, 'f1': 0.8014505893019038, 'number': 1065}
Overall Precision: 0.7220
Overall Recall: 0.7898
Overall F1: 0.7544
Overall Accuracy: 0.8078

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

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7641	1.0	10	1.5569	{'precision': 0.01979045401629802, 'recall': 0.021013597033374538, 'f1': 0.02038369304556355, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.20930232558139536, 'recall': 0.15211267605633802, 'f1': 0.1761827079934747, 'number': 1065}	0.1096	0.0898	0.0987	0.3917
1.4096	2.0	20	1.1718	{'precision': 0.18729096989966554, 'recall': 0.138442521631644, 'f1': 0.15920398009950248, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4800601956358164, 'recall': 0.5990610328638498, 'f1': 0.5329991645781119, 'number': 1065}	0.3892	0.3763	0.3827	0.6045
1.0362	3.0	30	0.9322	{'precision': 0.5212620027434842, 'recall': 0.46971569839307786, 'f1': 0.494148244473342, 'number': 809}	{'precision': 0.10344827586206896, 'recall': 0.025210084033613446, 'f1': 0.040540540540540536, 'number': 119}	{'precision': 0.6362847222222222, 'recall': 0.6882629107981221, 'f1': 0.661253946774921, 'number': 1065}	0.5843	0.5600	0.5719	0.7091
0.8024	4.0	40	0.7725	{'precision': 0.6457858769931663, 'recall': 0.7008652657601978, 'f1': 0.6721991701244814, 'number': 809}	{'precision': 0.1791044776119403, 'recall': 0.10084033613445378, 'f1': 0.12903225806451613, 'number': 119}	{'precision': 0.6911130284728214, 'recall': 0.752112676056338, 'f1': 0.7203237410071942, 'number': 1065}	0.6559	0.6924	0.6737	0.7700
0.6483	5.0	50	0.7035	{'precision': 0.6575790621592148, 'recall': 0.7453646477132262, 'f1': 0.6987253765932794, 'number': 809}	{'precision': 0.26881720430107525, 'recall': 0.21008403361344538, 'f1': 0.2358490566037736, 'number': 119}	{'precision': 0.7120067170445005, 'recall': 0.7962441314553991, 'f1': 0.75177304964539, 'number': 1065}	0.6706	0.7406	0.7039	0.7857
0.5298	6.0	60	0.6747	{'precision': 0.6925601750547046, 'recall': 0.7824474660074165, 'f1': 0.73476494486361, 'number': 809}	{'precision': 0.3472222222222222, 'recall': 0.21008403361344538, 'f1': 0.2617801047120419, 'number': 119}	{'precision': 0.7333333333333333, 'recall': 0.8366197183098592, 'f1': 0.7815789473684212, 'number': 1065}	0.7038	0.7772	0.7387	0.7984
0.4644	7.0	70	0.6752	{'precision': 0.6750261233019854, 'recall': 0.7985166872682324, 'f1': 0.7315968289920726, 'number': 809}	{'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119}	{'precision': 0.7529812606473595, 'recall': 0.8300469483568075, 'f1': 0.7896382313532827, 'number': 1065}	0.6973	0.7837	0.7380	0.8010
0.4253	8.0	80	0.6664	{'precision': 0.699666295884316, 'recall': 0.7775030902348579, 'f1': 0.7365339578454333, 'number': 809}	{'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119}	{'precision': 0.7704485488126649, 'recall': 0.8225352112676056, 'f1': 0.7956403269754768, 'number': 1065}	0.7186	0.7712	0.7439	0.8017
0.3815	9.0	90	0.6658	{'precision': 0.6973684210526315, 'recall': 0.7861557478368356, 'f1': 0.7391051714119697, 'number': 809}	{'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119}	{'precision': 0.7474916387959866, 'recall': 0.8394366197183099, 'f1': 0.7908005307386111, 'number': 1065}	0.7029	0.7883	0.7431	0.8053
0.3391	10.0	100	0.6736	{'precision': 0.7022900763358778, 'recall': 0.796044499381953, 'f1': 0.7462340672074159, 'number': 809}	{'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}	{'precision': 0.7681034482758621, 'recall': 0.8366197183098592, 'f1': 0.8008988764044945, 'number': 1065}	0.7159	0.7903	0.7513	0.8073
0.3117	11.0	110	0.6947	{'precision': 0.7086956521739131, 'recall': 0.8059332509270705, 'f1': 0.7541931752458069, 'number': 809}	{'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119}	{'precision': 0.7992667277726856, 'recall': 0.8187793427230047, 'f1': 0.8089053803339518, 'number': 1065}	0.7334	0.7852	0.7584	0.8083
0.2991	12.0	120	0.6963	{'precision': 0.7058823529411765, 'recall': 0.8009888751545118, 'f1': 0.7504342790966995, 'number': 809}	{'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119}	{'precision': 0.7716262975778547, 'recall': 0.8375586854460094, 'f1': 0.8032417829806394, 'number': 1065}	0.7193	0.7933	0.7545	0.8076
0.282	13.0	130	0.6991	{'precision': 0.7153846153846154, 'recall': 0.8046971569839307, 'f1': 0.7574171029668412, 'number': 809}	{'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119}	{'precision': 0.7898032200357782, 'recall': 0.8291079812206573, 'f1': 0.8089784699954191, 'number': 1065}	0.7320	0.7908	0.7603	0.8102
0.2722	14.0	140	0.7044	{'precision': 0.712253829321663, 'recall': 0.8046971569839307, 'f1': 0.7556587347649449, 'number': 809}	{'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119}	{'precision': 0.7811120917917035, 'recall': 0.8309859154929577, 'f1': 0.8052775250227479, 'number': 1065}	0.7254	0.7913	0.7569	0.8081
0.2634	15.0	150	0.7042	{'precision': 0.712403951701427, 'recall': 0.8022249690976514, 'f1': 0.7546511627906977, 'number': 809}	{'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119}	{'precision': 0.7747589833479404, 'recall': 0.8300469483568075, 'f1': 0.8014505893019038, 'number': 1065}	0.7220	0.7898	0.7544	0.8078

Framework versions

Transformers 4.23.1
Pytorch 1.12.1
Datasets 2.6.1
Tokenizers 0.13.1

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Oct 21, 2022
Updated: Oct 21, 2022