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Output_LayoutLMv3_v5

Token Classification
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layoutlmv3
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Output_LayoutLMv3_v5

This model is a fine-tuned version of microsoft/layoutlmv3-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3054
  • Precision: 0.8505
  • Recall: 0.8273
  • F1: 0.8387
  • Accuracy: 0.9723

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: 1e-06
  • train_batch_size: 3
  • weight_decay = 0.1 (Regularization)
  • eval_batch_size: 3
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 3000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 2.38 100 0.2910 0.7636 0.7636 0.7636 0.9637
No log 4.76 200 0.2822 0.8318 0.8091 0.8203 0.9706
No log 7.14 300 0.2942 0.8148 0.8 0.8073 0.9689
No log 9.52 400 0.2821 0.7909 0.7909 0.7909 0.9671
0.0005 11.9 500 0.2896 0.7909 0.7909 0.7909 0.9671
0.0005 14.29 600 0.2914 0.8241 0.8091 0.8165 0.9706
0.0005 16.67 700 0.2912 0.8095 0.7727 0.7907 0.9689
0.0005 19.05 800 0.2578 0.8241 0.8091 0.8165 0.9706
0.0005 21.43 900 0.2830 0.8241 0.8091 0.8165 0.9706
0.0005 23.81 1000 0.2878 0.8411 0.8182 0.8295 0.9723
0.0005 26.19 1100 0.3151 0.8113 0.7818 0.7963 0.9689
0.0005 28.57 1200 0.3142 0.7706 0.7636 0.7671 0.9637
0.0005 30.95 1300 0.2972 0.8273 0.8273 0.8273 0.9723
0.0005 33.33 1400 0.2866 0.8148 0.8 0.8073 0.9706
0.0004 35.71 1500 0.2737 0.8288 0.8364 0.8326 0.9723
0.0004 38.1 1600 0.2653 0.8532 0.8455 0.8493 0.9740
0.0004 40.48 1700 0.2740 0.8108 0.8182 0.8145 0.9706
0.0004 42.86 1800 0.2861 0.8198 0.8273 0.8235 0.9706
0.0004 45.24 1900 0.2904 0.7788 0.8 0.7892 0.9671
0.0004 47.62 2000 0.2899 0.7788 0.8 0.7892 0.9671
0.0004 50.0 2100 0.2957 0.8108 0.8182 0.8145 0.9689
0.0004 52.38 2200 0.2962 0.8505 0.8273 0.8387 0.9723
0.0004 54.76 2300 0.2962 0.8505 0.8273 0.8387 0.9723
0.0004 57.14 2400 0.3057 0.8505 0.8273 0.8387 0.9723
0.0002 59.52 2500 0.3070 0.8505 0.8273 0.8387 0.9723
0.0002 61.9 2600 0.3050 0.8505 0.8273 0.8387 0.9723
0.0002 64.29 2700 0.3050 0.8505 0.8273 0.8387 0.9723
0.0002 66.67 2800 0.3052 0.8505 0.8273 0.8387 0.9723
0.0002 69.05 2900 0.3052 0.8505 0.8273 0.8387 0.9723
0.0 71.43 3000 0.3054 0.8505 0.8273 0.8387 0.9723

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.13.3
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Evaluation results

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