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metadata
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
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: 0.9939271255060729
          - name: Recall
            type: recall
            value: 0.9959432048681541
          - name: F1
            type: f1
            value: 0.9949341438703141
          - name: Accuracy
            type: accuracy
            value: 0.9993680219085739

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: 0.0076
  • Precision: 0.9939
  • Recall: 0.9959
  • F1: 0.9949
  • Accuracy: 0.9994

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: 750

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 2.0 100 0.1086 0.87 0.8824 0.8761 0.9863
No log 4.0 200 0.0258 0.972 0.9858 0.9789 0.9971
No log 6.0 300 0.0172 0.972 0.9858 0.9789 0.9971
No log 8.0 400 0.0126 0.972 0.9858 0.9789 0.9971
0.1338 10.0 500 0.0076 0.9939 0.9959 0.9949 0.9994
0.1338 12.0 600 0.0073 0.9919 0.9959 0.9939 0.9992
0.1338 14.0 700 0.0048 0.9980 0.9980 0.9980 0.9998

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.3
  • Tokenizers 0.13.3