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

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: 0.0028
  • 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 0.0502 0.97 0.9838 0.9768 0.9968
No log 4.0 200 0.0194 0.972 0.9858 0.9789 0.9971
No log 6.0 300 0.0160 0.972 0.9858 0.9789 0.9971
No log 8.0 400 0.0123 0.972 0.9858 0.9789 0.9971
0.053 10.0 500 0.0089 0.9757 0.9757 0.9757 0.9966
0.053 12.0 600 0.0058 0.9959 0.9919 0.9939 0.9992
0.053 14.0 700 0.0046 0.9939 0.9919 0.9929 0.9989
0.053 16.0 800 0.0037 0.9960 0.9980 0.9970 0.9996
0.053 18.0 900 0.0068 0.9959 0.9878 0.9919 0.9987
0.0057 20.0 1000 0.0054 0.9919 0.9959 0.9939 0.9992
0.0057 22.0 1100 0.0057 0.9919 0.9959 0.9939 0.9992
0.0057 24.0 1200 0.0049 0.9919 0.9959 0.9939 0.9992
0.0057 26.0 1300 0.0052 0.9919 0.9959 0.9939 0.9992
0.0057 28.0 1400 0.0030 0.9960 0.9980 0.9970 0.9996
0.0022 30.0 1500 0.0028 0.9960 0.9980 0.9970 0.9996
0.0022 32.0 1600 0.0030 0.9960 0.9980 0.9970 0.9996
0.0022 34.0 1700 0.0030 0.9960 0.9980 0.9970 0.9996
0.0022 36.0 1800 0.0037 0.9960 0.9980 0.9970 0.9996
0.0022 38.0 1900 0.0037 0.9960 0.9980 0.9970 0.9996
0.0017 40.0 2000 0.0037 0.9960 0.9980 0.9970 0.9996

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.1