layoutlm-funsd

arsp9494/layoutlm-funsd

layoutlm-funsd is a token classification model from arsp9494. 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: Jul 5, 2023
Updated: Jul 5, 2023
SHA: e03d0990

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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: 0.6847
Answer: {'precision': 0.7144432194046306, 'recall': 0.8009888751545118, 'f1': 0.7552447552447553, 'number': 809}
Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
Question: {'precision': 0.7912966252220248, 'recall': 0.8366197183098592, 'f1': 0.81332724783204, 'number': 1065}
Overall Precision: 0.7309
Overall Recall: 0.7918
Overall F1: 0.7601
Overall Accuracy: 0.8132

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
1.8057	1.0	10	1.5966	{'precision': 0.008733624454148471, 'recall': 0.009888751545117428, 'f1': 0.009275362318840578, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.14909090909090908, 'recall': 0.11549295774647887, 'f1': 0.13015873015873014, 'number': 1065}	0.0752	0.0657	0.0702	0.3764
1.4635	2.0	20	1.2374	{'precision': 0.14137483787289234, 'recall': 0.13473423980222496, 'f1': 0.1379746835443038, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.42204995693367786, 'recall': 0.460093896713615, 'f1': 0.440251572327044, 'number': 1065}	0.3100	0.3006	0.3052	0.6035
1.1031	3.0	30	0.9623	{'precision': 0.4551451187335092, 'recall': 0.4264524103831891, 'f1': 0.44033184428844924, 'number': 809}	{'precision': 0.13157894736842105, 'recall': 0.04201680672268908, 'f1': 0.06369426751592357, 'number': 119}	{'precision': 0.630297565374211, 'recall': 0.6563380281690141, 'f1': 0.6430542778288868, 'number': 1065}	0.5507	0.5263	0.5382	0.7016
0.8514	4.0	40	0.7967	{'precision': 0.6146682188591386, 'recall': 0.6526576019777504, 'f1': 0.6330935251798562, 'number': 809}	{'precision': 0.23333333333333334, 'recall': 0.11764705882352941, 'f1': 0.1564245810055866, 'number': 119}	{'precision': 0.6810422282120395, 'recall': 0.711737089201878, 'f1': 0.6960514233241506, 'number': 1065}	0.6398	0.6523	0.6460	0.7495
0.6854	5.0	50	0.7228	{'precision': 0.6617647058823529, 'recall': 0.723114956736712, 'f1': 0.6910809214412286, 'number': 809}	{'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119}	{'precision': 0.697751873438801, 'recall': 0.7868544600938967, 'f1': 0.7396293027360988, 'number': 1065}	0.6644	0.7260	0.6938	0.7818
0.5608	6.0	60	0.6733	{'precision': 0.6585879873551106, 'recall': 0.7725587144622992, 'f1': 0.7110352673492606, 'number': 809}	{'precision': 0.25, 'recall': 0.17647058823529413, 'f1': 0.20689655172413793, 'number': 119}	{'precision': 0.7112561174551386, 'recall': 0.8187793427230047, 'f1': 0.7612396333478829, 'number': 1065}	0.6720	0.7617	0.7140	0.7976
0.486	7.0	70	0.6683	{'precision': 0.670514165792235, 'recall': 0.7898640296662547, 'f1': 0.7253121452894438, 'number': 809}	{'precision': 0.25688073394495414, 'recall': 0.23529411764705882, 'f1': 0.24561403508771928, 'number': 119}	{'precision': 0.7351398601398601, 'recall': 0.7896713615023474, 'f1': 0.7614305115436849, 'number': 1065}	0.6836	0.7566	0.7183	0.7992
0.4391	8.0	80	0.6590	{'precision': 0.6809623430962343, 'recall': 0.8046971569839307, 'f1': 0.7376770538243627, 'number': 809}	{'precision': 0.2641509433962264, 'recall': 0.23529411764705882, 'f1': 0.24888888888888888, 'number': 119}	{'precision': 0.7585324232081911, 'recall': 0.8347417840375587, 'f1': 0.7948144836835047, 'number': 1065}	0.7019	0.7868	0.7419	0.8042
0.3834	9.0	90	0.6569	{'precision': 0.7043189368770764, 'recall': 0.7861557478368356, 'f1': 0.7429906542056073, 'number': 809}	{'precision': 0.2619047619047619, 'recall': 0.2773109243697479, 'f1': 0.2693877551020408, 'number': 119}	{'precision': 0.7637457044673539, 'recall': 0.8347417840375587, 'f1': 0.7976671152983401, 'number': 1065}	0.7104	0.7817	0.7444	0.8099
0.3489	10.0	100	0.6655	{'precision': 0.6984649122807017, 'recall': 0.7873918417799752, 'f1': 0.7402672864613596, 'number': 809}	{'precision': 0.2714285714285714, 'recall': 0.31932773109243695, 'f1': 0.29343629343629346, 'number': 119}	{'precision': 0.7745614035087719, 'recall': 0.8291079812206573, 'f1': 0.800907029478458, 'number': 1065}	0.7108	0.7817	0.7446	0.8126
0.3103	11.0	110	0.6682	{'precision': 0.6981934112646121, 'recall': 0.8121137206427689, 'f1': 0.7508571428571429, 'number': 809}	{'precision': 0.3173076923076923, 'recall': 0.2773109243697479, 'f1': 0.29596412556053814, 'number': 119}	{'precision': 0.7878521126760564, 'recall': 0.8403755868544601, 'f1': 0.8132666969559291, 'number': 1065}	0.7267	0.7953	0.7595	0.8148
0.293	12.0	120	0.6739	{'precision': 0.7123893805309734, 'recall': 0.796044499381953, 'f1': 0.7518972562755399, 'number': 809}	{'precision': 0.328, 'recall': 0.3445378151260504, 'f1': 0.33606557377049184, 'number': 119}	{'precision': 0.7863475177304965, 'recall': 0.8328638497652582, 'f1': 0.8089375284997721, 'number': 1065}	0.7288	0.7888	0.7576	0.8167
0.2761	13.0	130	0.6783	{'precision': 0.705945945945946, 'recall': 0.8071693448702101, 'f1': 0.7531718569780853, 'number': 809}	{'precision': 0.3467741935483871, 'recall': 0.36134453781512604, 'f1': 0.35390946502057613, 'number': 119}	{'precision': 0.7935656836461126, 'recall': 0.8338028169014085, 'f1': 0.8131868131868133, 'number': 1065}	0.7306	0.7948	0.7614	0.8137
0.2633	14.0	140	0.6849	{'precision': 0.7085590465872156, 'recall': 0.8084054388133498, 'f1': 0.7551963048498845, 'number': 809}	{'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}	{'precision': 0.7883082373782108, 'recall': 0.8356807511737089, 'f1': 0.8113035551504102, 'number': 1065}	0.7273	0.7948	0.7595	0.8125
0.2632	15.0	150	0.6847	{'precision': 0.7144432194046306, 'recall': 0.8009888751545118, 'f1': 0.7552447552447553, 'number': 809}	{'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}	{'precision': 0.7912966252220248, 'recall': 0.8366197183098592, 'f1': 0.81332724783204, 'number': 1065}	0.7309	0.7918	0.7601	0.8132

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Jul 5, 2023
Updated: Jul 5, 2023