layoutlm-funsd

zyr4c31/layoutlm-funsd

layoutlm-funsd is a token classification model from zyr4c31. 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: Feb 13, 2025
Updated: Feb 13, 2025
SHA: 19f45c9c

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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: 0.7202
Answer: {'precision': 0.7178378378378378, 'recall': 0.8207663782447466, 'f1': 0.7658592848904268, 'number': 809}
Header: {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119}
Question: {'precision': 0.7876895628902766, 'recall': 0.8291079812206573, 'f1': 0.807868252516011, 'number': 1065}
Overall Precision: 0.7319
Overall Recall: 0.7973
Overall F1: 0.7632
Overall Accuracy: 0.8026

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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
1.7706	1.0	10	1.5222	{'precision': 0.02973977695167286, 'recall': 0.029666254635352288, 'f1': 0.029702970297029705, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3521923620933522, 'recall': 0.23380281690140844, 'f1': 0.281038374717833, 'number': 1065}	0.1803	0.1370	0.1557	0.3975
1.4016	2.0	20	1.1857	{'precision': 0.20402298850574713, 'recall': 0.17552533992583436, 'f1': 0.18870431893687706, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4961059190031153, 'recall': 0.5981220657276995, 'f1': 0.5423584504044274, 'number': 1065}	0.3930	0.3909	0.3919	0.6152
1.0564	3.0	30	0.8913	{'precision': 0.5174418604651163, 'recall': 0.5500618046971569, 'f1': 0.5332534451767527, 'number': 809}	{'precision': 0.1, 'recall': 0.04201680672268908, 'f1': 0.059171597633136105, 'number': 119}	{'precision': 0.6189339697692919, 'recall': 0.7305164319248826, 'f1': 0.6701119724375538, 'number': 1065}	0.5667	0.6162	0.5904	0.7310
0.7957	4.0	40	0.7535	{'precision': 0.6295907660020986, 'recall': 0.7416563658838071, 'f1': 0.681044267877412, 'number': 809}	{'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119}	{'precision': 0.6666666666666666, 'recall': 0.7511737089201878, 'f1': 0.7064017660044148, 'number': 1065}	0.6379	0.7125	0.6731	0.7735
0.6406	5.0	50	0.7106	{'precision': 0.6659242761692651, 'recall': 0.7391841779975278, 'f1': 0.7006444053895724, 'number': 809}	{'precision': 0.27380952380952384, 'recall': 0.19327731092436976, 'f1': 0.2266009852216749, 'number': 119}	{'precision': 0.6777178103315343, 'recall': 0.8253521126760563, 'f1': 0.7442845046570702, 'number': 1065}	0.6582	0.7526	0.7022	0.7820
0.5391	6.0	60	0.6904	{'precision': 0.6619433198380567, 'recall': 0.8084054388133498, 'f1': 0.72787979966611, 'number': 809}	{'precision': 0.32098765432098764, 'recall': 0.2184873949579832, 'f1': 0.26, 'number': 119}	{'precision': 0.7570009033423668, 'recall': 0.7868544600938967, 'f1': 0.7716390423572744, 'number': 1065}	0.6976	0.7617	0.7282	0.7894
0.4684	7.0	70	0.6723	{'precision': 0.6848167539267016, 'recall': 0.8084054388133498, 'f1': 0.7414965986394558, 'number': 809}	{'precision': 0.27522935779816515, 'recall': 0.25210084033613445, 'f1': 0.2631578947368421, 'number': 119}	{'precision': 0.7657497781721384, 'recall': 0.8103286384976526, 'f1': 0.7874087591240876, 'number': 1065}	0.7061	0.7762	0.7395	0.8032
0.417	8.0	80	0.6596	{'precision': 0.6854754440961337, 'recall': 0.8108776266996292, 'f1': 0.7429218573046432, 'number': 809}	{'precision': 0.3177570093457944, 'recall': 0.2857142857142857, 'f1': 0.3008849557522124, 'number': 119}	{'precision': 0.7611301369863014, 'recall': 0.8347417840375587, 'f1': 0.7962382445141066, 'number': 1065}	0.7074	0.7923	0.7475	0.8087
0.3681	9.0	90	0.6724	{'precision': 0.7109458023379384, 'recall': 0.826946847960445, 'f1': 0.7645714285714287, 'number': 809}	{'precision': 0.3394495412844037, 'recall': 0.31092436974789917, 'f1': 0.324561403508772, 'number': 119}	{'precision': 0.7646551724137931, 'recall': 0.8328638497652582, 'f1': 0.7973033707865169, 'number': 1065}	0.7208	0.7993	0.7580	0.8084
0.3646	10.0	100	0.6917	{'precision': 0.7109207708779444, 'recall': 0.8207663782447466, 'f1': 0.7619047619047621, 'number': 809}	{'precision': 0.3490566037735849, 'recall': 0.31092436974789917, 'f1': 0.32888888888888884, 'number': 119}	{'precision': 0.7867975022301517, 'recall': 0.828169014084507, 'f1': 0.8069533394327539, 'number': 1065}	0.7325	0.7943	0.7622	0.8083
0.3053	11.0	110	0.7003	{'precision': 0.6927083333333334, 'recall': 0.8220024721878862, 'f1': 0.7518371961560204, 'number': 809}	{'precision': 0.3115942028985507, 'recall': 0.36134453781512604, 'f1': 0.3346303501945525, 'number': 119}	{'precision': 0.7845601436265709, 'recall': 0.8206572769953052, 'f1': 0.8022028453419, 'number': 1065}	0.7152	0.7938	0.7524	0.8003
0.3037	12.0	120	0.6989	{'precision': 0.7167381974248928, 'recall': 0.8257107540173053, 'f1': 0.7673750717978173, 'number': 809}	{'precision': 0.32456140350877194, 'recall': 0.31092436974789917, 'f1': 0.31759656652360513, 'number': 119}	{'precision': 0.7824561403508772, 'recall': 0.8375586854460094, 'f1': 0.8090702947845805, 'number': 1065}	0.7306	0.8013	0.7643	0.8080
0.2778	13.0	130	0.7137	{'precision': 0.7164502164502164, 'recall': 0.8182941903584673, 'f1': 0.7639930755914599, 'number': 809}	{'precision': 0.33613445378151263, 'recall': 0.33613445378151263, 'f1': 0.33613445378151263, 'number': 119}	{'precision': 0.7871198568872988, 'recall': 0.8262910798122066, 'f1': 0.8062299587723316, 'number': 1065}	0.7321	0.7938	0.7617	0.8042
0.2599	14.0	140	0.7203	{'precision': 0.7182017543859649, 'recall': 0.8096415327564895, 'f1': 0.7611853573503776, 'number': 809}	{'precision': 0.3412698412698413, 'recall': 0.36134453781512604, 'f1': 0.35102040816326535, 'number': 119}	{'precision': 0.7831858407079646, 'recall': 0.8309859154929577, 'f1': 0.806378132118451, 'number': 1065}	0.7302	0.7943	0.7609	0.8032
0.2583	15.0	150	0.7202	{'precision': 0.7178378378378378, 'recall': 0.8207663782447466, 'f1': 0.7658592848904268, 'number': 809}	{'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119}	{'precision': 0.7876895628902766, 'recall': 0.8291079812206573, 'f1': 0.807868252516011, 'number': 1065}	0.7319	0.7973	0.7632	0.8026

Framework versions

Transformers 4.48.3
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Feb 13, 2025
Updated: Feb 13, 2025