readme: ---
  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: train
        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
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # layoutlmv3-finetuned-invoice
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/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
  