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--- |
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library_name: transformers |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: layoutlm-document-v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlm-document-v2 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0036 |
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- Ate de la facture: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21} |
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- Iret du fournisseur: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 20} |
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- Om du fournisseur: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21} |
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- Ontant tva: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} |
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- Ontant total ht: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} |
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- Ontant total ttc: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 21} |
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- Umero de bc: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} |
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- Overall Precision: 1.0 |
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- Overall Recall: 1.0 |
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- Overall F1: 1.0 |
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- Overall Accuracy: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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