readme: ---
  model-index:
  - name: EASA-5
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: sroie
        type: sroie
        config: discharge
        split: train
        args: discharge
      metrics:
      - name: Precision
        type: precision
        value: 0.9813084112149533
      - name: Recall
        type: recall
        value: 0.9905660377358491
      - name: F1
        type: f1
        value: 0.9859154929577464
      - name: Accuracy
        type: accuracy
        value: 0.9980704293294742
  ---
  
  <!-- 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. -->
  
  # EASA-5
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the sroie dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0256
  - Precision: 0.9813
  - Recall: 0.9906
  - F1: 0.9859
  - Accuracy: 0.9981
  
  ## 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: 5000
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 5.56   | 100  | 0.0146          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | No log        | 11.11  | 200  | 0.0099          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | No log        | 16.67  | 300  | 0.0153          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | No log        | 22.22  | 400  | 0.0181          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0212        | 27.78  | 500  | 0.0185          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0212        | 33.33  | 600  | 0.0190          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0212        | 38.89  | 700  | 0.0214          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0212        | 44.44  | 800  | 0.0215          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0212        | 50.0   | 900  | 0.0215          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0004        | 55.56  | 1000 | 0.0215          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0004        | 61.11  | 1100 | 0.0216          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0004        | 66.67  | 1200 | 0.0222          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0004        | 72.22  | 1300 | 0.0226          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0004        | 77.78  | 1400 | 0.0229          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0001        | 83.33  | 1500 | 0.0231          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0001        | 88.89  | 1600 | 0.0234          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0001        | 94.44  | 1700 | 0.0237          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0001        | 100.0  | 1800 | 0.0239          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0001        | 105.56 | 1900 | 0.0234          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0002        | 111.11 | 2000 | 0.0232          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0002        | 116.67 | 2100 | 0.0237          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0002        | 122.22 | 2200 | 0.0168          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0002        | 127.78 | 2300 | 0.0215          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0002        | 133.33 | 2400 | 0.0214          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0012        | 138.89 | 2500 | 0.0222          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0012        | 144.44 | 2600 | 0.0229          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0012        | 150.0  | 2700 | 0.0231          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0012        | 155.56 | 2800 | 0.0233          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0012        | 161.11 | 2900 | 0.0235          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0           | 166.67 | 3000 | 0.0237          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0           | 172.22 | 3100 | 0.0234          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0           | 177.78 | 3200 | 0.0238          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0           | 183.33 | 3300 | 0.0240          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0           | 188.89 | 3400 | 0.0242          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0           | 194.44 | 3500 | 0.0240          | 0.9813    | 0.9906 | 0.9859 | 0.9981   |
  | 0.0           | 200.0  | 3600 | 0.0238          | 0.9813    | 