layoutlm-funsd

vatipham/layoutlm-funsd

layoutlm-funsd is a token classification model from vatipham. 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 17, 2023
Updated: Oct 18, 2023
SHA: 26777072

Try a prompt

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: 0.8174
Answer: {'precision': 0.7233333333333334, 'recall': 0.8046971569839307, 'f1': 0.7618490345231129, 'number': 809}
Header: {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119}
Question: {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}
Overall Precision: 0.7351
Overall Recall: 0.7978
Overall F1: 0.7652
Overall Accuracy: 0.8019

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.3435	1.0	10	1.1455	{'precision': 0.29554655870445345, 'recall': 0.27070457354758964, 'f1': 0.2825806451612903, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.43828125, 'recall': 0.5267605633802817, 'f1': 0.47846481876332625, 'number': 1065}	0.3858	0.3914	0.3885	0.6180
0.9706	2.0	20	0.8933	{'precision': 0.5545454545454546, 'recall': 0.6786155747836835, 'f1': 0.6103390772651472, 'number': 809}	{'precision': 0.08695652173913043, 'recall': 0.03361344537815126, 'f1': 0.048484848484848485, 'number': 119}	{'precision': 0.6115916955017301, 'recall': 0.6638497652582159, 'f1': 0.6366501575866726, 'number': 1065}	0.5748	0.6322	0.6022	0.7308
0.7426	3.0	30	0.7478	{'precision': 0.6294058408862034, 'recall': 0.7725587144622992, 'f1': 0.6936736958934517, 'number': 809}	{'precision': 0.1891891891891892, 'recall': 0.11764705882352941, 'f1': 0.1450777202072539, 'number': 119}	{'precision': 0.6858333333333333, 'recall': 0.7727699530516432, 'f1': 0.726710816777042, 'number': 1065}	0.6449	0.7336	0.6864	0.7770
0.6123	4.0	40	0.6950	{'precision': 0.6286266924564797, 'recall': 0.8034610630407911, 'f1': 0.705371676614216, 'number': 809}	{'precision': 0.19387755102040816, 'recall': 0.15966386554621848, 'f1': 0.17511520737327188, 'number': 119}	{'precision': 0.6943268416596104, 'recall': 0.7699530516431925, 'f1': 0.730186999109528, 'number': 1065}	0.6438	0.7471	0.6916	0.7886
0.5267	5.0	50	0.6804	{'precision': 0.6574172892209178, 'recall': 0.761433868974042, 'f1': 0.7056128293241695, 'number': 809}	{'precision': 0.21818181818181817, 'recall': 0.20168067226890757, 'f1': 0.2096069868995633, 'number': 119}	{'precision': 0.7246496290189612, 'recall': 0.8253521126760563, 'f1': 0.771729587357331, 'number': 1065}	0.6721	0.7622	0.7143	0.8013
0.4587	6.0	60	0.6701	{'precision': 0.670490093847758, 'recall': 0.7948084054388134, 'f1': 0.7273755656108597, 'number': 809}	{'precision': 0.2108843537414966, 'recall': 0.2605042016806723, 'f1': 0.2330827067669173, 'number': 119}	{'precision': 0.7309602649006622, 'recall': 0.8291079812206573, 'f1': 0.7769467663880335, 'number': 1065}	0.6729	0.7812	0.7230	0.7977
0.3981	7.0	70	0.6637	{'precision': 0.7029063509149623, 'recall': 0.8071693448702101, 'f1': 0.7514384349827388, 'number': 809}	{'precision': 0.2698412698412698, 'recall': 0.2857142857142857, 'f1': 0.27755102040816326, 'number': 119}	{'precision': 0.7621483375959079, 'recall': 0.8394366197183099, 'f1': 0.7989276139410187, 'number': 1065}	0.7096	0.7933	0.7491	0.8062
0.3608	8.0	80	0.6778	{'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809}	{'precision': 0.25874125874125875, 'recall': 0.31092436974789917, 'f1': 0.2824427480916031, 'number': 119}	{'precision': 0.7633851468048359, 'recall': 0.8300469483568075, 'f1': 0.7953216374269007, 'number': 1065}	0.7081	0.7863	0.7451	0.8003
0.311	9.0	90	0.6931	{'precision': 0.6991247264770241, 'recall': 0.7898640296662547, 'f1': 0.7417295414973882, 'number': 809}	{'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119}	{'precision': 0.7606244579358196, 'recall': 0.8234741784037559, 'f1': 0.7908025247971145, 'number': 1065}	0.7060	0.7797	0.7411	0.8055
0.276	10.0	100	0.7144	{'precision': 0.7298787210584344, 'recall': 0.8182941903584673, 'f1': 0.7715617715617716, 'number': 809}	{'precision': 0.3103448275862069, 'recall': 0.37815126050420167, 'f1': 0.34090909090909094, 'number': 119}	{'precision': 0.7814159292035399, 'recall': 0.8291079812206573, 'f1': 0.8045558086560365, 'number': 1065}	0.7287	0.7978	0.7617	0.8062
0.2393	11.0	110	0.7342	{'precision': 0.7155555555555555, 'recall': 0.796044499381953, 'f1': 0.7536571094207138, 'number': 809}	{'precision': 0.296551724137931, 'recall': 0.36134453781512604, 'f1': 0.32575757575757575, 'number': 119}	{'precision': 0.774869109947644, 'recall': 0.8338028169014085, 'f1': 0.8032564450474899, 'number': 1065}	0.7188	0.7903	0.7529	0.8042
0.2227	12.0	120	0.7539	{'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809}	{'precision': 0.33884297520661155, 'recall': 0.3445378151260504, 'f1': 0.3416666666666667, 'number': 119}	{'precision': 0.7686440677966102, 'recall': 0.8516431924882629, 'f1': 0.8080178173719377, 'number': 1065}	0.7191	0.7978	0.7564	0.8006
0.2119	13.0	130	0.7774	{'precision': 0.7263736263736263, 'recall': 0.8170580964153276, 'f1': 0.7690517742873763, 'number': 809}	{'precision': 0.28125, 'recall': 0.37815126050420167, 'f1': 0.3225806451612903, 'number': 119}	{'precision': 0.7714033539276258, 'recall': 0.8206572769953052, 'f1': 0.7952684258416743, 'number': 1065}	0.7172	0.7928	0.7531	0.7952
0.1882	14.0	140	0.7688	{'precision': 0.7270668176670442, 'recall': 0.7935723114956736, 'f1': 0.7588652482269503, 'number': 809}	{'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119}	{'precision': 0.7883597883597884, 'recall': 0.8394366197183099, 'f1': 0.8130968622100955, 'number': 1065}	0.7359	0.7928	0.7633	0.8024
0.1767	15.0	150	0.7717	{'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809}	{'precision': 0.3548387096774194, 'recall': 0.3697478991596639, 'f1': 0.36213991769547327, 'number': 119}	{'precision': 0.789612676056338, 'recall': 0.8422535211267606, 'f1': 0.8150840527033166, 'number': 1065}	0.7374	0.8033	0.7690	0.8020
0.1703	16.0	160	0.7943	{'precision': 0.7231638418079096, 'recall': 0.7911001236093943, 'f1': 0.755608028335301, 'number': 809}	{'precision': 0.36231884057971014, 'recall': 0.42016806722689076, 'f1': 0.38910505836575876, 'number': 119}	{'precision': 0.79185119574845, 'recall': 0.8394366197183099, 'f1': 0.8149498632634458, 'number': 1065}	0.7361	0.7948	0.7643	0.8017
0.1643	17.0	170	0.8087	{'precision': 0.7207207207207207, 'recall': 0.7911001236093943, 'f1': 0.7542722451384797, 'number': 809}	{'precision': 0.33098591549295775, 'recall': 0.3949579831932773, 'f1': 0.3601532567049809, 'number': 119}	{'precision': 0.7932263814616756, 'recall': 0.8356807511737089, 'f1': 0.8139003200731596, 'number': 1065}	0.7328	0.7913	0.7609	0.7990
0.1443	18.0	180	0.8170	{'precision': 0.7230419977298524, 'recall': 0.7873918417799752, 'f1': 0.7538461538461538, 'number': 809}	{'precision': 0.36231884057971014, 'recall': 0.42016806722689076, 'f1': 0.38910505836575876, 'number': 119}	{'precision': 0.7898936170212766, 'recall': 0.8366197183098592, 'f1': 0.8125854993160054, 'number': 1065}	0.7350	0.7918	0.7623	0.7994
0.148	19.0	190	0.8169	{'precision': 0.7245240761478163, 'recall': 0.799752781211372, 'f1': 0.7602820211515863, 'number': 809}	{'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119}	{'precision': 0.792149866190901, 'recall': 0.8338028169014085, 'f1': 0.8124428179322964, 'number': 1065}	0.7364	0.7948	0.7645	0.8015
0.1441	20.0	200	0.8174	{'precision': 0.7233333333333334, 'recall': 0.8046971569839307, 'f1': 0.7618490345231129, 'number': 809}	{'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119}	{'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}	0.7351	0.7978	0.7652	0.8019

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Oct 17, 2023
Updated: Oct 18, 2023