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layoutlm-funsd

Token Classification
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PyTorch
layoutlm
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metadata
base_model: microsoft/layoutlm-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: layoutlm-funsd
    results: []

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: 0.0043
  • Ame: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
  • Andom number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
  • Ather Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
  • Lace Of Birth: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}
  • Other Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
  • Overall Precision: 1.0
  • Overall Recall: 1.0
  • Overall F1: 1.0
  • Overall Accuracy: 1.0

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 Ame Andom number Ather Name Lace Of Birth Other Name Overall Precision Overall Recall Overall F1 Overall Accuracy
1.9017 1.0 6 1.1501 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} 0.0 0.0 0.0 0.7967
0.8813 2.0 12 0.5397 {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 19} 0.0 0.0 0.0 0.7967
0.4889 3.0 18 0.3035 {'precision': 0.5862068965517241, 'recall': 0.8947368421052632, 'f1': 0.7083333333333333, 'number': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} {'precision': 0.75, 'recall': 0.15789473684210525, 'f1': 0.2608695652173913, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} {'precision': 0.56, 'recall': 0.7368421052631579, 'f1': 0.6363636363636364, 'number': 19} 0.6883 0.6543 0.6709 0.9431
0.2784 4.0 24 0.1590 {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} {'precision': 0.8260869565217391, 'recall': 1.0, 'f1': 0.9047619047619047, 'number': 19} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} {'precision': 0.9333333333333333, 'recall': 0.7368421052631579, 'f1': 0.8235294117647058, 'number': 19} 0.9221 0.8765 0.8987 0.9797
0.1669 5.0 30 0.0903 {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} {'precision': 0.9444444444444444, 'recall': 0.8947368421052632, 'f1': 0.918918918918919, 'number': 19} {'precision': 1.0, 'recall': 0.4, 'f1': 0.5714285714285715, 'number': 5} {'precision': 0.8260869565217391, 'recall': 1.0, 'f1': 0.9047619047619047, 'number': 19} 0.9383 0.9383 0.9383 0.9898
0.1034 6.0 36 0.0486 {'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': 19} {'precision': 1.0, 'recall': 0.8, 'f1': 0.888888888888889, 'number': 5} {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} 0.9877 0.9877 0.9877 0.9980
0.0637 7.0 42 0.0232 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.0403 8.0 48 0.0125 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.0259 9.0 54 0.0087 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.02 10.0 60 0.0068 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.0166 11.0 66 0.0058 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.0148 12.0 72 0.0053 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.0126 13.0 78 0.0047 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.0122 14.0 84 0.0044 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0
0.014 15.0 90 0.0043 {'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': 19} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1