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update model card README.md
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metadata
license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - generated
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: generated
          type: generated
          config: sroie
          split: test
          args: sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9959514170040485
          - name: Recall
            type: recall
            value: 0.9979716024340771
          - name: F1
            type: f1
            value: 0.9969604863221885
          - name: Accuracy
            type: accuracy
            value: 0.9995786812723826

layoutlmv3-finetuned-invoice

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

  • Loss: 0.0070
  • Precision: 0.9960
  • Recall: 0.9980
  • F1: 0.9970
  • Accuracy: 0.9996

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-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 2.0 100 0.0947 0.892 0.9047 0.8983 0.9886
No log 4.0 200 0.0232 0.972 0.9858 0.9789 0.9971
No log 6.0 300 0.0175 0.972 0.9858 0.9789 0.9971
No log 8.0 400 0.0130 0.972 0.9858 0.9789 0.9971
0.1292 10.0 500 0.0070 0.9960 0.9980 0.9970 0.9996
0.1292 12.0 600 0.0081 0.9837 0.9817 0.9827 0.9975
0.1292 14.0 700 0.0045 0.9980 0.9959 0.9970 0.9996
0.1292 16.0 800 0.0026 0.9980 0.9980 0.9980 0.9998
0.1292 18.0 900 0.0024 0.9980 0.9980 0.9980 0.9998
0.0066 20.0 1000 0.0030 0.9960 0.9980 0.9970 0.9996
0.0066 22.0 1100 0.0025 0.9980 0.9980 0.9980 0.9998
0.0066 24.0 1200 0.0019 0.9980 0.9980 0.9980 0.9998
0.0066 26.0 1300 0.0019 0.9980 0.9980 0.9980 0.9998
0.0066 28.0 1400 0.0028 0.9960 0.9980 0.9970 0.9996
0.0026 30.0 1500 0.0022 0.9960 0.9980 0.9970 0.9996
0.0026 32.0 1600 0.0023 0.9960 0.9980 0.9970 0.9996
0.0026 34.0 1700 0.0021 0.9960 0.9980 0.9970 0.9996
0.0026 36.0 1800 0.0033 0.9960 0.9980 0.9970 0.9996
0.0026 38.0 1900 0.0032 0.9960 0.9980 0.9970 0.9996
0.0019 40.0 2000 0.0032 0.9960 0.9980 0.9970 0.9996

Framework versions

  • Transformers 4.27.0.dev0
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2