readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - wild_receipt
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-wildreceipt
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wild_receipt
        type: wild_receipt
        args: WildReceipt
      metrics:
      - name: Precision
        type: precision
        value: 0.877212237618329
      - name: Recall
        type: recall
        value: 0.8798678959680749
      - name: F1
        type: f1
        value: 0.8785380599065679
      - name: Accuracy
        type: accuracy
        value: 0.9249204782274871
  ---
  
  <!-- 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 wild_receipt dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3108
  - Precision: 0.8772
  - Recall: 0.8799
  - F1: 0.8785
  - Accuracy: 0.9249
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  The WildReceipt dataset consists of 1740 receipt images, and contains 25 key information categories, and a total of about 69000 text boxes. 1268 and 472 images are used for training and testing respectively to train the LayoutLMv3 model for Key Information Extraction.
  
  ## Training procedure
  
  The training code: https://github.com/Theivaprakasham/layoutlmv3/blob/main/training_codes/LayoutLMv3_training_WildReceipts_dataset.ipynb
  
  ### 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  | 1.3143          | 0.6709    | 0.2679 | 0.3829 | 0.6700   |
  | No log        | 0.63  | 200  | 0.8814          | 0.6478    | 0.5195 | 0.5766 | 0.7786   |
  | No log        | 0.95  | 300  | 0.6568          | 0.7205    | 0.6491 | 0.6829 | 0.8303   |
  | No log        | 1.26  | 400  | 0.5618          | 0.7544    | 0.7072 | 0.7300 | 0.8519   |
  | 1.0284        | 1.58  | 500  | 0.5003          | 0.7802    | 0.7566 | 0.7682 | 0.8687   |
  | 1.0284        | 1.89  | 600  | 0.4454          | 0.7941    | 0.7679 | 0.7807 | 0.8748   |
  | 1.0284        | 2.21  | 700  | 0.4314          | 0.8142    | 0.7928 | 0.8033 | 0.8852   |
  | 1.0284        | 2.52  | 800  | 0.3870          | 0.8172    | 0.8200 | 0.8186 | 0.8953   |
  | 1.0284        | 2.84  | 900  | 0.3629          | 0.8288    | 0.8369 | 0.8329 | 0.9025   |
  | 0.4167        | 3.15  | 1000 | 0.3537          | 0.8540    | 0.8200 | 0.8366 | 0.9052   |
  | 0.4167        | 3.47  | 1100 | 0.3383          | 0.8438    | 0.8285 | 0.8361 | 0.9063   |
  | 0.4167        | 3.79  | 1200 | 0.3403          | 0.8297    | 0.8493 | 0.8394 | 0.9062   |
  | 0.4167        | 4.1   | 1300 | 0.3271          | 0.8428    | 0.8545 | 0.8487 | 0.9110   |
  | 0.4167        | 4.42  | 1400 | 0.3182          | 0.8491    | 0.8518 | 0.8504 | 0.9131   |
  | 0.2766        | 4.73  | 1500 | 0.3111          | 0.8491    | 0.8539 | 0.8515 | 0.9129   |
  | 0.2766        | 5.05  | 1600 | 0.3177          | 0.8397    | 0.8620 | 0.8507 | 0.9124   |
  | 0.2766        | 5.36  | 1700 | 0.3091          | 0.8676    | 0.8548 | 0.8612 | 0.9191   |
  | 0.2766        | 5.68  | 1800 | 0.3080          | 0.8508    | 0.8645 | 0.8576 | 0.9162   |
  | 0.2766        | 5.99  | 1900 | 0.3059          | 0.8492    | 0.8662 | 0.8576 | 0.9163   |
  | 0.2114        | 6.31  | 2000 | 0.3184          | 0.8536    | 0.8657 | 0.8596 | 0.9147   |
  | 0.2114        | 6.62  | 2100 | 0.3161          | 0.8583    | 0.8713 | 0.8648 | 0.9184   |
  | 0.2114        | 6.94  | 2200 | 0.3055          | 0.8707    | 0.8682 | 0.8694 | 0.9220   |
  | 0.2114        | 7.26  | 2300 | 0.3004          | 0.8689    | 0.8745 | 0.8717 | 0.9219   |
  | 0.2114        | 7.57  | 2400 | 0.3111          | 0.8701    | 0.8720 | 0.8711 | 0.9211   |
  | 0.174         | 7.89  | 2500 | 0.3130          | 0.8599    | 0.8741 | 0.8669 | 0.9198   |
  | 0.174         | 8.2   | 2600 | 0.3034          | 0.8661    | 0.8748 | 0.8704 | 0.9219   |
  | 0.174         | 8.52  | 2700 | 0.3005          | 0.8799    | 0.8673 | 0.8736 | 0.9225   |
  | 0.174         | 8.83  | 2800 | 0.3043          | 0.8687    | 0.8804 | 0.8745 | 0.9240   |
  | 0.174         | 9.15  | 2900 | 0.3121          | 0.8776    | 0.8704 | 0.8740 | 0.9242   |
  | 0.1412        | 9.46  | 3000 | 0.3131          | 0.8631    | 0.8755 | 0.8692 | 0.9204   |
  | 0.1412        | 9.78  | 3100 | 0.3067        