--- 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](https://huggingface.co/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 | 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| 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