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: 0.8927
  • Column: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}
  • Ignore: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
  • Key: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}
  • Value: {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33}
  • Overall Precision: 0.6875
  • Overall Recall: 0.4231
  • Overall F1: 0.5238
  • Overall Accuracy: 0.7947

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: 15

Training results

Training Loss Epoch Step Validation Loss Column Ignore Key Value Overall Precision Overall Recall Overall F1 Overall Accuracy
2.4627 1.0 2 2.1288 {'precision': 0.23529411764705882, 'recall': 0.16, 'f1': 0.19047619047619052, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} {'precision': 0.06060606060606061, 'recall': 0.06060606060606061, 'f1': 0.06060606060606061, 'number': 33} 0.0870 0.0769 0.0816 0.6887
2.1025 2.0 4 1.7650 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} 0.0 0.0 0.0 0.6921
1.7503 3.0 6 1.4611 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} 0.0 0.0 0.0 0.6904
1.4557 4.0 8 1.2624 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} 0.0 0.0 0.0 0.6904
1.3067 5.0 10 1.1889 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} 0.0 0.0 0.0 0.6904
1.1884 6.0 12 1.1436 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} 0.0 0.0 0.0 0.6904
1.1456 7.0 14 1.0901 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33} 0.0 0.0 0.0 0.6904
1.0915 8.0 16 1.0410 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 0.11764705882352941, 'f1': 0.21052631578947367, 'number': 17} {'precision': 0.3333333333333333, 'recall': 0.030303030303030304, 'f1': 0.05555555555555555, 'number': 33} 0.6 0.0385 0.0723 0.6937
1.0428 9.0 18 0.9990 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 0.29411764705882354, 'f1': 0.45454545454545453, 'number': 17} {'precision': 0.23529411764705882, 'recall': 0.12121212121212122, 'f1': 0.16, 'number': 33} 0.2727 0.1154 0.1622 0.7252
0.9819 10.0 20 0.9639 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 0.4117647058823529, 'f1': 0.5833333333333334, 'number': 17} {'precision': 0.2631578947368421, 'recall': 0.15151515151515152, 'f1': 0.19230769230769232, 'number': 33} 0.3243 0.1538 0.2087 0.7517
0.9592 11.0 22 0.9344 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 0.6470588235294118, 'f1': 0.7857142857142858, 'number': 17} {'precision': 0.3684210526315789, 'recall': 0.21212121212121213, 'f1': 0.2692307692307693, 'number': 33} 0.4737 0.2308 0.3103 0.7781
0.9011 12.0 24 0.9105 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} {'precision': 0.64, 'recall': 0.48484848484848486, 'f1': 0.5517241379310344, 'number': 33} 0.66 0.4231 0.5156 0.7930
0.9426 13.0 26 0.8927 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33} 0.6875 0.4231 0.5238 0.7947
0.8809 14.0 28 0.8821 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33} 0.6875 0.4231 0.5238 0.7947
0.9188 15.0 30 0.8774 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17} {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33} 0.6875 0.4231 0.5238 0.7947

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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