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layoutlm-document-v2

Token Classification
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layoutlmv3
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  1. README.md +84 -0
  2. logs/events.out.tfevents.1729276220.eaaed41b5f43.4291.0 +2 -2
  3. merges.txt +0 -0
  4. model.safetensors +1 -1
  5. preprocessor_config.json +26 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +79 -0
  9. vocab.json +0 -0
README.md ADDED
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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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+
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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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+
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+ # layoutlm-document-v2
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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