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layoutlm-donut-own

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metadata
tags:
  - generated_from_trainer
model-index:
  - name: layoutlm-donut-own
    results: []

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