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: 0.874880087707277
      - name: Recall
        type: recall
        value: 0.878491812302188
      - name: F1
        type: f1
        value: 0.8766822301565504
      - name: Accuracy
        type: accuracy
        value: 0.9253043764396183
  ---
  
  <!-- 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: 0.3111
  - Precision: 0.8749
  - Recall: 0.8785
  - F1: 0.8767
  - Accuracy: 0.9253
  
  ## 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  | 1.3060          | 0.6792    | 0.3615 | 0.4718 | 0.6966   |
  | No log        | 0.63  | 200  | 0.8842          | 0.6524    | 0.5193 | 0.5783 | 0.7737   |
  | No log        | 0.95  | 300  | 0.6795          | 0.7338    | 0.6772 | 0.7044 | 0.8336   |
  | No log        | 1.26  | 400  | 0.5604          | 0.7719    | 0.7390 | 0.7551 | 0.8629   |
  | 1.0319        | 1.58  | 500  | 0.4862          | 0.7819    | 0.7618 | 0.7717 | 0.8730   |
  | 1.0319        | 1.89  | 600  | 0.4365          | 0.7852    | 0.7807 | 0.7829 | 0.8795   |
  | 1.0319        | 2.21  | 700  | 0.4182          | 0.8162    | 0.8016 | 0.8088 | 0.8897   |
  | 1.0319        | 2.52  | 800  | 0.3886          | 0.8126    | 0.8196 | 0.8161 | 0.8936   |
  | 1.0319        | 2.84  | 900  | 0.3637          | 0.8260    | 0.8347 | 0.8303 | 0.9004   |
  | 0.4162        | 3.15  | 1000 | 0.3482          | 0.8532    | 0.8243 | 0.8385 | 0.9062   |
  | 0.4162        | 3.47  | 1100 | 0.3474          | 0.8573    | 0.8248 | 0.8407 | 0.9042   |
  | 0.4162        | 3.79  | 1200 | 0.3325          | 0.8408    | 0.8435 | 0.8421 | 0.9086   |
  | 0.4162        | 4.1   | 1300 | 0.3262          | 0.8468    | 0.8467 | 0.8468 | 0.9095   |
  | 0.4162        | 4.42  | 1400 | 0.3237          | 0.8511    | 0.8442 | 0.8477 | 0.9100   |
  | 0.2764        | 4.73  | 1500 | 0.3156          | 0.8563    | 0.8456 | 0.8509 | 0.9122   |
  | 0.2764        | 5.05  | 1600 | 0.3032          | 0.8558    | 0.8566 | 0.8562 | 0.9153   |
  | 0.2764        | 5.36  | 1700 | 0.3120          | 0.8604    | 0.8457 | 0.8530 | 0.9142   |
  | 0.2764        | 5.68  | 1800 | 0.2976          | 0.8608    | 0.8592 | 0.8600 | 0.9178   |
  | 0.2764        | 5.99  | 1900 | 0.3056          | 0.8551    | 0.8676 | 0.8613 | 0.9171   |
  | 0.212         | 6.31  | 2000 | 0.3191          | 0.8528    | 0.8599 | 0.8563 | 0.9147   |
  | 0.212         | 6.62  | 2100 | 0.3051          | 0.8653    | 0.8635 | 0.8644 | 0.9186   |
  | 0.212         | 6.94  | 2200 | 0.3022          | 0.8681    | 0.8632 | 0.8657 | 0.9208   |
  | 0.212         | 7.26  | 2300 | 0.3101          | 0.8605    | 0.8643 | 0.8624 | 0.9178   |
  | 0.212         | 7.57  | 2400 | 0.3100          | 0.8553    | 0.8693 | 0.8622 | 0.9163   |
  | 0.1725        | 7.89  | 2500 | 0.3012          | 0.8685    | 0.8723 | 0.8704 | 0.9221   |
  | 0.1725        | 8.2   | 2600 | 0.3135          | 0.8627    | 0.8756 | 0.8691 | 0.9187   |
  | 0.1725        | 8.52  | 2700 | 0.3115          | 0.8768    | 0.8671 | 0.8719 | 0.9229   |
  | 0.1725        | 8.83  | 2800 | 0.3044          | 0.8757    | 0.8708 | 0.8732 | 0.9231   |
  | 0.1725        | 9.15  | 2900 | 0.3042          | 0.8698    | 0.8658 | 0.8678 | 0.9212   |
  | 0.142         | 9.46  | 3000 | 0.3095          | 0.8677    | 0.8702 | 0.8690 | 0.9207   |
  | 0.142         | 9.78  | 3100 | 0.3119          | 0.8686    | 0.8762 | 0.8724 | 0.9229   |
  | 0.142         | 10.09 | 3200 | 0.3078          | 0.8713    | 0.8774 | 0.8743 | 0.9238   |
  | 0.142         | 10.41 | 3300 | 0.3123          | 0.8711    | 0.8753 | 0.8732 | 0.9238   |
  | 0.142         | 10.73 | 3400 | 0.3098          | 0.8688    | 0.8774 | 0.8731 | 0.9232  