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layoutlm-funsd

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0754
  • Ignal: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
  • Oise: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}
  • Overall Precision: 0.0
  • Overall Recall: 0.0
  • Overall F1: 0.0
  • Overall Accuracy: 0.9670

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Ignal Oise Overall Precision Overall Recall Overall F1 Overall Accuracy
0.7198 1.0 1 0.7152 {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0052 0.0435 0.0093 0.5024
0.7121 2.0 2 0.7152 {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0052 0.0435 0.0093 0.5024
0.7191 3.0 3 0.4802 {'precision': 0.045454545454545456, 'recall': 0.09090909090909091, 'f1': 0.060606060606060615, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0222 0.0435 0.0294 0.9245
0.4799 4.0 4 0.3268 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9646
0.3263 5.0 5 0.2246 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.2269 6.0 6 0.1598 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.1625 7.0 7 0.1227 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.1246 8.0 8 0.1030 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.1042 9.0 9 0.0937 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.0942 10.0 10 0.0892 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.0888 11.0 11 0.0861 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.0834 12.0 12 0.0832 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.0768 13.0 13 0.0805 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.0745 14.0 14 0.0778 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670
0.071 15.0 15 0.0754 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} 0.0 0.0 0.0 0.9670

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0
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