layoutlm-funsd

Anorak/layoutlm-funsd

layoutlm-funsd is a token classification model from Anorak. 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: Feb 10, 2023
Updated: Feb 10, 2023
SHA: 29eed85a

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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: 0.6857
Answer: {'precision': 0.7176981541802389, 'recall': 0.8170580964153276, 'f1': 0.7641618497109827, 'number': 809}
Header: {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}
Question: {'precision': 0.7773820124666073, 'recall': 0.819718309859155, 'f1': 0.7979890310786105, 'number': 1065}
Overall Precision: 0.7204
Overall Recall: 0.7898
Overall F1: 0.7535
Overall Accuracy: 0.8139

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
1.8064	1.0	10	1.6080	{'precision': 0.020618556701030927, 'recall': 0.012360939431396786, 'f1': 0.01545595054095827, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2702127659574468, 'recall': 0.11924882629107982, 'f1': 0.16547231270358306, 'number': 1065}	0.1435	0.0687	0.0929	0.3378
1.4826	2.0	20	1.2520	{'precision': 0.20166320166320167, 'recall': 0.23980222496909764, 'f1': 0.21908526256352345, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4309507286606523, 'recall': 0.5830985915492958, 'f1': 0.49561053471667993, 'number': 1065}	0.3392	0.4089	0.3708	0.5993
1.1438	3.0	30	0.9584	{'precision': 0.463519313304721, 'recall': 0.5339925834363412, 'f1': 0.49626651349798967, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6199664429530202, 'recall': 0.6938967136150235, 'f1': 0.6548515728843598, 'number': 1065}	0.5492	0.5876	0.5678	0.6897
0.8546	4.0	40	0.7900	{'precision': 0.5885714285714285, 'recall': 0.7639060568603214, 'f1': 0.6648735879505111, 'number': 809}	{'precision': 0.06666666666666667, 'recall': 0.025210084033613446, 'f1': 0.036585365853658534, 'number': 119}	{'precision': 0.6505823627287853, 'recall': 0.7342723004694836, 'f1': 0.6898985443317159, 'number': 1065}	0.6108	0.7040	0.6541	0.7537
0.6765	5.0	50	0.7144	{'precision': 0.6514047866805411, 'recall': 0.7737948084054388, 'f1': 0.7073446327683616, 'number': 809}	{'precision': 0.09230769230769231, 'recall': 0.05042016806722689, 'f1': 0.06521739130434782, 'number': 119}	{'precision': 0.7019810508182601, 'recall': 0.7652582159624414, 'f1': 0.7322551662174304, 'number': 1065}	0.6616	0.7260	0.6923	0.7773
0.5613	6.0	60	0.6796	{'precision': 0.6635514018691588, 'recall': 0.7898640296662547, 'f1': 0.7212189616252822, 'number': 809}	{'precision': 0.15306122448979592, 'recall': 0.12605042016806722, 'f1': 0.1382488479262673, 'number': 119}	{'precision': 0.7274320771253286, 'recall': 0.7793427230046949, 'f1': 0.7524932003626473, 'number': 1065}	0.6739	0.7446	0.7075	0.7927
0.4872	7.0	70	0.6554	{'precision': 0.6592517694641051, 'recall': 0.8059332509270705, 'f1': 0.7252502780867631, 'number': 809}	{'precision': 0.22549019607843138, 'recall': 0.19327731092436976, 'f1': 0.20814479638009048, 'number': 119}	{'precision': 0.7383177570093458, 'recall': 0.815962441314554, 'f1': 0.775200713648528, 'number': 1065}	0.6808	0.7747	0.7247	0.7997
0.4334	8.0	80	0.6526	{'precision': 0.6941176470588235, 'recall': 0.8022249690976514, 'f1': 0.7442660550458714, 'number': 809}	{'precision': 0.24545454545454545, 'recall': 0.226890756302521, 'f1': 0.23580786026200873, 'number': 119}	{'precision': 0.7493627867459643, 'recall': 0.828169014084507, 'f1': 0.7867975022301517, 'number': 1065}	0.7012	0.7817	0.7393	0.8035
0.3941	9.0	90	0.6694	{'precision': 0.7048997772828508, 'recall': 0.7824474660074165, 'f1': 0.741652021089631, 'number': 809}	{'precision': 0.22099447513812154, 'recall': 0.33613445378151263, 'f1': 0.26666666666666666, 'number': 119}	{'precision': 0.7218984179850125, 'recall': 0.8140845070422535, 'f1': 0.76522506619594, 'number': 1065}	0.6754	0.7727	0.7208	0.8007
0.3556	10.0	100	0.6607	{'precision': 0.694006309148265, 'recall': 0.8158220024721878, 'f1': 0.75, 'number': 809}	{'precision': 0.25, 'recall': 0.2773109243697479, 'f1': 0.26294820717131473, 'number': 119}	{'precision': 0.7846153846153846, 'recall': 0.8140845070422535, 'f1': 0.7990783410138248, 'number': 1065}	0.7130	0.7827	0.7462	0.8068
0.3245	11.0	110	0.6728	{'precision': 0.6990595611285266, 'recall': 0.826946847960445, 'f1': 0.7576443941109853, 'number': 809}	{'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}	{'precision': 0.7817703768624014, 'recall': 0.8375586854460094, 'f1': 0.8087035358114233, 'number': 1065}	0.7192	0.8008	0.7578	0.8089
0.3113	12.0	120	0.6799	{'precision': 0.71875, 'recall': 0.796044499381953, 'f1': 0.755425219941349, 'number': 809}	{'precision': 0.25903614457831325, 'recall': 0.36134453781512604, 'f1': 0.3017543859649123, 'number': 119}	{'precision': 0.775330396475771, 'recall': 0.8262910798122066, 'f1': 0.8, 'number': 1065}	0.7132	0.7863	0.7480	0.8106
0.2921	13.0	130	0.6836	{'precision': 0.7070063694267515, 'recall': 0.823238566131026, 'f1': 0.7607081667618503, 'number': 809}	{'precision': 0.32432432432432434, 'recall': 0.3025210084033613, 'f1': 0.31304347826086953, 'number': 119}	{'precision': 0.7976513098464318, 'recall': 0.8291079812206573, 'f1': 0.8130755064456722, 'number': 1065}	0.7338	0.7953	0.7633	0.8122
0.2841	14.0	140	0.6848	{'precision': 0.7150537634408602, 'recall': 0.8220024721878862, 'f1': 0.7648073605520415, 'number': 809}	{'precision': 0.26666666666666666, 'recall': 0.33613445378151263, 'f1': 0.2973977695167286, 'number': 119}	{'precision': 0.7841726618705036, 'recall': 0.8187793427230047, 'f1': 0.8011024345429489, 'number': 1065}	0.7194	0.7913	0.7536	0.8127
0.2793	15.0	150	0.6857	{'precision': 0.7176981541802389, 'recall': 0.8170580964153276, 'f1': 0.7641618497109827, 'number': 809}	{'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}	{'precision': 0.7773820124666073, 'recall': 0.819718309859155, 'f1': 0.7979890310786105, 'number': 1065}	0.7204	0.7898	0.7535	0.8139

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.8.0+cu101
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Feb 10, 2023
Updated: Feb 10, 2023