layoutlm-document-v2

kamleshsolanki/layoutlm-document-v2

layoutlm-document-v2 is a token classification model from kamleshsolanki. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.

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

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At a glance

Task: Token Classification
Library: transformers
License: cc-by-nc-sa-4.0
Model type: layoutlmv3
Access: Public
Created: Oct 18, 2024
Updated: Oct 18, 2024
SHA: e8a3ee03

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Base models

microsoft/layoutlmv3-base

Model card

From the Hugging Face model README

layoutlm-document-v2

This model is a fine-tuned version of microsoft/layoutlmv3-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0036
Ate de la facture: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}
Iret du fournisseur: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}
Om du fournisseur: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}
Ontant tva: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
Ontant total ht: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
Ontant total ttc: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}
Umero de bc: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
Overall Precision: 1.0
Overall Recall: 1.0
Overall F1: 1.0
Overall Accuracy: 1.0

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	Ate de la facture	Iret du fournisseur	Om du fournisseur	Ontant tva	Ontant total ht	Ontant total ttc	Umero de bc	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.6209	1.0	6	1.0535	{'precision': 0.9523809523809523, 'recall': 0.9523809523809523, 'f1': 0.9523809523809523, 'number': 21}	{'precision': 0.625, 'recall': 1.0, 'f1': 0.7692307692307693, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 0.7619047619047619, 'recall': 0.8421052631578947, 'f1': 0.8, 'number': 19}	{'precision': 1.0, 'recall': 0.2631578947368421, 'f1': 0.4166666666666667, 'number': 19}	{'precision': 0.4117647058823529, 'recall': 0.3333333333333333, 'f1': 0.36842105263157887, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}	0.7607	0.6794	0.7177	0.7863
0.8518	2.0	12	0.4979	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 0.7692307692307693, 'recall': 1.0, 'f1': 0.8695652173913044, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 0.8823529411764706, 'recall': 0.7894736842105263, 'f1': 0.8333333333333333, 'number': 19}	{'precision': 0.8333333333333334, 'recall': 0.7142857142857143, 'f1': 0.7692307692307692, 'number': 21}	{'precision': 1.0, 'recall': 0.4, 'f1': 0.5714285714285715, 'number': 10}	0.9055	0.8779	0.8915	0.9084
0.4183	3.0	18	0.2090	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 0.9047619047619048, 'recall': 1.0, 'f1': 0.9500000000000001, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 0.9047619047619048, 'f1': 0.9500000000000001, 'number': 21}	{'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10}	0.9771	0.9771	0.9771	0.9771
0.2071	4.0	24	0.1112	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 0.9047619047619048, 'recall': 1.0, 'f1': 0.9500000000000001, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 0.9047619047619048, 'f1': 0.9500000000000001, 'number': 21}	{'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10}	0.9771	0.9771	0.9771	0.9771
0.084	5.0	30	0.0304	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0429	6.0	36	0.0146	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0183	7.0	42	0.0090	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.011	8.0	48	0.0045	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0081	9.0	54	0.0041	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0062	10.0	60	0.0124	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	0.9924	0.9924	0.9924	0.9924
0.005	11.0	66	0.0250	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	0.9924	0.9924	0.9924	0.9924
0.0047	12.0	72	0.0193	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	0.9924	0.9924	0.9924	0.9924
0.0073	13.0	78	0.0023	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0041	14.0	84	0.0034	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0044	15.0	90	0.0036	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1

Task: Token Classification
Library: transformers
Type: layoutlmv3
License: cc-by-nc-sa-4.0
Created: Oct 18, 2024
Updated: Oct 18, 2024