readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - wildreceipt
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-wildreceipt
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wildreceipt
        type: wildreceipt
        config: WildReceipt
        split: train
        args: WildReceipt
      metrics:
      - name: Precision
        type: precision
        value: 0.8693453601202679
      - name: Recall
        type: recall
        value: 0.8753268198706481
      - name: F1
        type: f1
        value: 0.872325836533187
      - name: Accuracy
        type: accuracy
        value: 0.9240429965997587
  ---
  
  <!-- 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-wildreceipt
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wildreceipt dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3154
  - Precision: 0.8693
  - Recall: 0.8753
  - F1: 0.8723
  - Accuracy: 0.9240
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 4000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.32  | 100  | 1.3618          | 0.6375    | 0.3049 | 0.4125 | 0.6708   |
  | No log        | 0.63  | 200  | 0.9129          | 0.6662    | 0.4897 | 0.5645 | 0.7631   |
  | No log        | 0.95  | 300  | 0.6800          | 0.7273    | 0.6375 | 0.6795 | 0.8274   |
  | No log        | 1.26  | 400  | 0.5733          | 0.7579    | 0.6926 | 0.7238 | 0.8501   |
  | 1.0638        | 1.58  | 500  | 0.5015          | 0.7854    | 0.7383 | 0.7611 | 0.8667   |
  | 1.0638        | 1.89  | 600  | 0.4501          | 0.7916    | 0.7680 | 0.7796 | 0.8770   |
  | 1.0638        | 2.21  | 700  | 0.4145          | 0.8177    | 0.8053 | 0.8114 | 0.8917   |
  | 1.0638        | 2.52  | 800  | 0.3835          | 0.8214    | 0.8210 | 0.8212 | 0.8961   |
  | 1.0638        | 2.84  | 900  | 0.3666          | 0.8251    | 0.8338 | 0.8294 | 0.9009   |
  | 0.423         | 3.15  | 1000 | 0.3524          | 0.8485    | 0.8217 | 0.8349 | 0.9026   |
  | 0.423         | 3.47  | 1100 | 0.3451          | 0.8430    | 0.8327 | 0.8378 | 0.9060   |
  | 0.423         | 3.79  | 1200 | 0.3348          | 0.8347    | 0.8504 | 0.8425 | 0.9092   |
  | 0.423         | 4.1   | 1300 | 0.3331          | 0.8368    | 0.8448 | 0.8408 | 0.9079   |
  | 0.423         | 4.42  | 1400 | 0.3163          | 0.8503    | 0.8519 | 0.8511 | 0.9138   |
  | 0.2822        | 4.73  | 1500 | 0.3168          | 0.8531    | 0.8504 | 0.8518 | 0.9133   |
  | 0.2822        | 5.05  | 1600 | 0.3013          | 0.8629    | 0.8577 | 0.8603 | 0.9183   |
  | 0.2822        | 5.36  | 1700 | 0.3146          | 0.8619    | 0.8528 | 0.8573 | 0.9160   |
  | 0.2822        | 5.68  | 1800 | 0.3121          | 0.8474    | 0.8638 | 0.8555 | 0.9159   |
  | 0.2822        | 5.99  | 1900 | 0.3054          | 0.8537    | 0.8667 | 0.8601 | 0.9166   |
  | 0.2176        | 6.31  | 2000 | 0.3127          | 0.8556    | 0.8592 | 0.8574 | 0.9167   |
  | 0.2176        | 6.62  | 2100 | 0.3072          | 0.8568    | 0.8667 | 0.8617 | 0.9194   |
  | 0.2176        | 6.94  | 2200 | 0.2989          | 0.8617    | 0.8660 | 0.8638 | 0.9209   |
  | 0.2176        | 7.26  | 2300 | 0.2997          | 0.8616    | 0.8682 | 0.8649 | 0.9199   |
  | 0.2176        | 7.57  | 2400 | 0.3100          | 0.8538    | 0.8689 | 0.8613 | 0.9191   |
  | 0.1777        | 7.89  | 2500 | 0.3022          | 0.8649    | 0.8695 | 0.8672 | 0.9228   |
  | 0.1777        | 8.2   | 2600 | 0.2990          | 0.8631    | 0.8740 | 0.8685 | 0.9224   |
  | 0.1777        | 8.52  | 2700 | 0.3072          | 0.8669    | 0.8697 | 0.8683 | 0.9228   |
  | 0.1777        | 8.83  | 2800 | 0.3038          | 0.8689    | 0.8720 | 0.8705 | 0.9238   |
  | 0.1777        | 9.15  | 2900 | 0.3138          | 0.8726    | 0.8673 | 0.8700 | 0.9216   |
  | 0.1434        | 9.46  | 3000 | 0.3150          | 0.8610    | 0.8740 | 0.8674 | 0.9221   |
  | 0.1434        | 9.78  | 3100 | 0.3055          | 0.8674    | 0.8742 | 0.8708 | 0.9239   |
  | 0.1434        | 10.09 | 3200 | 0.3182          | 0.8618    | 0.8724 | 0.8671 | 0.9215   |
  | 0.1434        | 10.41 | 3300 | 0.3175          | 0.8633    | 0.8727 | 0.8680 | 0.9223   |
  | 0.1434        | 10.73 | 3400 | 0.3146          | 0.8685    | 0.8717 | 0.8701 | 0.9234 