readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - wild_receipt
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: OCR-LayoutLMv3-Invoice
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wild_receipt
        type: wild_receipt
        config: WildReceipt
        split: train
        args: WildReceipt
      metrics:
      - name: Precision
        type: precision
        value: 0.8765398302764851
      - name: Recall
        type: recall
        value: 0.8812439796339617
      - name: F1
        type: f1
        value: 0.8788856103753516
      - name: Accuracy
        type: accuracy
        value: 0.92678512668641
  ---
  
  <!-- 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. -->
  
  # OCR-LayoutLMv3-Invoice
  
  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: 0.3159
  - Precision: 0.8765
  - Recall: 0.8812
  - F1: 0.8789
  - Accuracy: 0.9268
  
  ## Model description
  
  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: 6000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.16  | 100  | 1.5032          | 0.4934    | 0.1444 | 0.2234 | 0.6064   |
  | No log        | 0.32  | 200  | 1.0282          | 0.5884    | 0.4420 | 0.5048 | 0.7385   |
  | No log        | 0.47  | 300  | 0.7856          | 0.7448    | 0.6205 | 0.6770 | 0.8133   |
  | No log        | 0.63  | 400  | 0.6464          | 0.7736    | 0.6689 | 0.7174 | 0.8399   |
  | 1.1733        | 0.79  | 500  | 0.5672          | 0.7609    | 0.7303 | 0.7453 | 0.8557   |
  | 1.1733        | 0.95  | 600  | 0.5055          | 0.7658    | 0.7652 | 0.7655 | 0.8677   |
  | 1.1733        | 1.1   | 700  | 0.4735          | 0.7946    | 0.7848 | 0.7897 | 0.8784   |
  | 1.1733        | 1.26  | 800  | 0.4414          | 0.7962    | 0.7946 | 0.7954 | 0.8818   |
  | 1.1733        | 1.42  | 900  | 0.4094          | 0.8176    | 0.8064 | 0.8120 | 0.8894   |
  | 0.5047        | 1.58  | 1000 | 0.3971          | 0.8219    | 0.8248 | 0.8234 | 0.8961   |
  | 0.5047        | 1.74  | 1100 | 0.4082          | 0.7993    | 0.8362 | 0.8174 | 0.8927   |
  | 0.5047        | 1.89  | 1200 | 0.3797          | 0.8240    | 0.8317 | 0.8278 | 0.8962   |
  | 0.5047        | 2.05  | 1300 | 0.3597          | 0.8326    | 0.8331 | 0.8329 | 0.9020   |
  | 0.5047        | 2.21  | 1400 | 0.3544          | 0.8462    | 0.8283 | 0.8371 | 0.9020   |
  | 0.368         | 2.37  | 1500 | 0.3374          | 0.8428    | 0.8435 | 0.8432 | 0.9056   |
  | 0.368         | 2.52  | 1600 | 0.3364          | 0.8406    | 0.8522 | 0.8464 | 0.9089   |
  | 0.368         | 2.68  | 1700 | 0.3404          | 0.8467    | 0.8536 | 0.8501 | 0.9107   |
  | 0.368         | 2.84  | 1800 | 0.3319          | 0.8405    | 0.8501 | 0.8453 | 0.9090   |
  | 0.368         | 3.0   | 1900 | 0.3324          | 0.8584    | 0.8492 | 0.8538 | 0.9117   |
  | 0.2949        | 3.15  | 2000 | 0.3204          | 0.8691    | 0.8404 | 0.8545 | 0.9119   |
  | 0.2949        | 3.31  | 2100 | 0.3107          | 0.8599    | 0.8547 | 0.8573 | 0.9162   |
  | 0.2949        | 3.47  | 2200 | 0.3169          | 0.8680    | 0.8489 | 0.8584 | 0.9146   |
  | 0.2949        | 3.63  | 2300 | 0.3190          | 0.8683    | 0.8519 | 0.8600 | 0.9152   |
  | 0.2949        | 3.79  | 2400 | 0.2975          | 0.8631    | 0.8617 | 0.8624 | 0.9182   |
  | 0.2438        | 3.94  | 2500 | 0.3040          | 0.8566    | 0.8640 | 0.8603 | 0.9171   |
  | 0.2438        | 4.1   | 2600 | 0.3045          | 0.8585    | 0.8642 | 0.8613 | 0.9181   |
  | 0.2438        | 4.26  | 2700 | 0.3139          | 0.8498    | 0.8748 | 0.8621 | 0.9160   |
  | 0.2438        | 4.42  | 2800 | 0.2985          | 0.8642    | 0.8672 | 0.8657 | 0.9214   |
  | 0.2438        | 4.57  | 2900 | 0.3047          | 0.8688    | 0.8694 | 0.8691 | 0.9214   |
  | 0.2028        | 4.73  | 3000 | 0.2986          | 0.8686    | 0.8695 | 0.8691 | 0.9207   |
  | 0.2028        | 4.89  | 3100 | 0.3135          | 0.8628    | 0.8755 | 0.8691 | 0.9197   |
  | 0.2028        | 5.05  | 3200 | 0.2927          | 0.8656    | 0.8755 | 0.8705 | 0.9217   |
  | 0.2028        | 5.21  | 3300 | 0.2992          | 0.8724    | 0.8697 | 0.8711 | 0.9228   |
  | 0.2028        | 5.36  | 3400 | 0.2975          | 0.8831    | 0.8639 | 0.8734 | 0.9244   |
  | 0.1814        | 5.52  | 3500 | 0.2897          | 0.8736    | 0.8788