layoutlm-donut-own

AKASHR/layoutlm-donut-own

layoutlm-donut-own is a token classification model from AKASHR. 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: May 31, 2023
Updated: May 31, 2023
SHA: 01660b0c

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From the Hugging Face model README

layoutlm-donut-own

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: 2.3438
Ban: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Eader:client: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Eader:client Tax Id: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Eader:iban: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Eader:invoice Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Eader:invoice No: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Eader:seller: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Eader:seller Tax Id: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}
Eller: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Eller Tax Id: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Lient: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Lient Tax Id: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Nvoice Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Nvoice No: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Otal Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Otal Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Otal Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Tem Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Tem Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Tem Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Tem Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Tem Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Tem Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Tems Row1:item Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Tems Row1:item Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Tems Row1:item Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Tems Row1:item Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}
Tems Row1:item Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45}
Tems Row1:item Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}
Tems Row1:seller Tax Id: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Tems Row2:item Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}
Tems Row2:item Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}
Tems Row2:item Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38}
Tems Row2:item Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}
Tems Row2:item Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 40}
Tems Row2:item Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38}
Tems Row3:item Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}
Tems Row3:item Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}
Tems Row3:item Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}
Tems Row3:item Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}
Tems Row3:item Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}
Tems Row3:item Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 31}
Tems Row4:item Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}
Tems Row4:item Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}
Tems Row4:item Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}
Tems Row4:item Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}
Tems Row4:item Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 27}
Tems Row4:item Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}
Tems Row5:item Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}
Tems Row5:item Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}
Tems Row5:item Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}
Tems Row5:item Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}
Tems Row5:item Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 22}
Tems Row5:item Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}
Tems Row6:item Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}
Tems Row6:item Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}
Tems Row6:item Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}
Tems Row6:item Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}
Tems Row6:item Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}
Tems Row6:item Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}
Tems Row7:item Desc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
Tems Row7:item Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
Tems Row7:item Net Price: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
Tems Row7:item Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
Tems Row7:item Qty: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
Tems Row7:item Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
Ther: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 609}
Ummary:total Gross Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Ummary:total Net Worth: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Ummary:total Vat: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}
Overall Precision: 0.0
Overall Recall: 0.0
Overall F1: 0.0
Overall Accuracy: 0.5689

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Ban	Eader:client	Eader:client Tax Id	Eader:iban	Eader:invoice Date	Eader:invoice No	Eader:seller	Eader:seller Tax Id	Eller	Eller Tax Id	Lient	Lient Tax Id	Nvoice Date	Nvoice No	Otal Gross Worth	Otal Net Worth	Otal Vat	Tem Desc	Tem Gross Worth	Tem Net Price	Tem Net Worth	Tem Qty	Tem Vat	Tems Row1:item Desc	Tems Row1:item Gross Worth	Tems Row1:item Net Price	Tems Row1:item Net Worth	Tems Row1:item Qty	Tems Row1:item Vat	Tems Row1:seller Tax Id	Tems Row2:item Desc	Tems Row2:item Gross Worth	Tems Row2:item Net Price	Tems Row2:item Net Worth	Tems Row2:item Qty	Tems Row2:item Vat	Tems Row3:item Desc	Tems Row3:item Gross Worth	Tems Row3:item Net Price	Tems Row3:item Net Worth	Tems Row3:item Qty	Tems Row3:item Vat	Tems Row4:item Desc	Tems Row4:item Gross Worth	Tems Row4:item Net Price	Tems Row4:item Net Worth	Tems Row4:item Qty	Tems Row4:item Vat	Tems Row5:item Desc	Tems Row5:item Gross Worth	Tems Row5:item Net Price	Tems Row5:item Net Worth	Tems Row5:item Qty	Tems Row5:item Vat	Tems Row6:item Desc	Tems Row6:item Gross Worth	Tems Row6:item Net Price	Tems Row6:item Net Worth	Tems Row6:item Qty	Tems Row6:item Vat	Tems Row7:item Desc	Tems Row7:item Gross Worth	Tems Row7:item Net Price	Tems Row7:item Net Worth	Tems Row7:item Qty	Tems Row7:item Vat	Ther	Ummary:total Gross Worth	Ummary:total Net Worth	Ummary:total Vat	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
3.6109	1.0	7	2.7573	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 40}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 31}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 27}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 22}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 609}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	0.0	0.0	0.0	0.5689
2.5323	2.0	14	2.3438	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 43}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 40}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 31}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 26}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 27}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 22}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 609}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 44}	0.0	0.0	0.0	0.5689

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: May 31, 2023
Updated: May 31, 2023