readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: DatasetSinergyRhenus
    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: 0.8851351351351351
      - name: Recall
        type: recall
        value: 0.8762541806020067
      - name: F1
        type: f1
        value: 0.8806722689075631
      - name: Accuracy
        type: accuracy
        value: 0.9709117575164996
  ---
  
  <!-- 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. -->
  
  # DatasetSinergyRhenus
  
  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: 0.2981
  - Precision: 0.8851
  - Recall: 0.8763
  - F1: 0.8807
  - Accuracy: 0.9709
  
  ## 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        | 1.19  | 100  | 0.3083          | 0.6238    | 0.7709 | 0.6896 | 0.8937   |
  | No log        | 2.38  | 200  | 0.1983          | 0.7691    | 0.8244 | 0.7958 | 0.9281   |
  | No log        | 3.57  | 300  | 0.2468          | 0.7690    | 0.8462 | 0.8057 | 0.9213   |
  | No log        | 4.76  | 400  | 0.1565          | 0.8412    | 0.8595 | 0.8503 | 0.9614   |
  | 0.2937        | 5.95  | 500  | 0.1671          | 0.8238    | 0.8445 | 0.8340 | 0.9577   |
  | 0.2937        | 7.14  | 600  | 0.1665          | 0.8440    | 0.8595 | 0.8517 | 0.9594   |
  | 0.2937        | 8.33  | 700  | 0.1679          | 0.8571    | 0.8528 | 0.8550 | 0.9628   |
  | 0.2937        | 9.52  | 800  | 0.1669          | 0.8656    | 0.8512 | 0.8583 | 0.9611   |
  | 0.2937        | 10.71 | 900  | 0.1579          | 0.8765    | 0.8662 | 0.8713 | 0.9680   |
  | 0.1075        | 11.9  | 1000 | 0.1883          | 0.8656    | 0.8512 | 0.8583 | 0.9633   |
  | 0.1075        | 13.1  | 1100 | 0.1873          | 0.8765    | 0.8662 | 0.8713 | 0.9592   |
  | 0.1075        | 14.29 | 1200 | 0.1725          | 0.8524    | 0.8595 | 0.8560 | 0.9668   |
  | 0.1075        | 15.48 | 1300 | 0.1690          | 0.8679    | 0.8679 | 0.8679 | 0.9650   |
  | 0.1075        | 16.67 | 1400 | 0.1959          | 0.8825    | 0.8662 | 0.8743 | 0.9668   |
  | 0.0637        | 17.86 | 1500 | 0.1919          | 0.8723    | 0.8679 | 0.8701 | 0.9638   |
  | 0.0637        | 19.05 | 1600 | 0.2020          | 0.8780    | 0.8662 | 0.8721 | 0.9663   |
  | 0.0637        | 20.24 | 1700 | 0.2093          | 0.8716    | 0.8512 | 0.8613 | 0.9641   |
  | 0.0637        | 21.43 | 1800 | 0.2184          | 0.8716    | 0.8629 | 0.8672 | 0.9643   |
  | 0.0637        | 22.62 | 1900 | 0.2204          | 0.8576    | 0.8562 | 0.8569 | 0.9631   |
  | 0.0452        | 23.81 | 2000 | 0.2478          | 0.8591    | 0.8562 | 0.8576 | 0.9621   |
  | 0.0452        | 25.0  | 2100 | 0.2506          | 0.8769    | 0.8579 | 0.8673 | 0.9665   |
  | 0.0452        | 26.19 | 2200 | 0.2270          | 0.8862    | 0.8729 | 0.8795 | 0.9690   |
  | 0.0452        | 27.38 | 2300 | 0.2544          | 0.8790    | 0.8629 | 0.8709 | 0.9646   |
  | 0.0452        | 28.57 | 2400 | 0.2251          | 0.8735    | 0.8662 | 0.8699 | 0.9643   |
  | 0.0313        | 29.76 | 2500 | 0.2597          | 0.8668    | 0.8595 | 0.8631 | 0.9633   |
  | 0.0313        | 30.95 | 2600 | 0.2635          | 0.8670    | 0.8612 | 0.8641 | 0.9643   |
  | 0.0313        | 32.14 | 2700 | 0.2493          | 0.8752    | 0.8679 | 0.8715 | 0.9665   |
  | 0.0313        | 33.33 | 2800 | 0.2565          | 0.8797    | 0.8679 | 0.8737 | 0.9660   |
  | 0.0313        | 34.52 | 2900 | 0.2626          | 0.8831    | 0.8712 | 0.8771 | 0.9672   |
  | 0.0218        | 35.71 | 3000 | 0.2750          | 0.8639    | 0.8595 | 0.8617 | 0.9650   |
  | 0.0218        | 36.9  | 3100 | 0.2683          | 0.8682    | 0.8595 | 0.8639 | 0.9660   |
  | 0.0218        | 38.1  | 3200 | 0.2751          | 0.8724    | 0.8579 | 0.8651 | 0.9660   |
  | 0.0218        | 39.29 | 3300 | 0.2851          | 0.8746    | 0.8629 | 0.8687 | 0.9655   |
  | 0.0218        | 40.48 | 3400 | 0.2737          | 0.8805    | 0.8629 | 0.8716 | 0.9692   |
  | 0.0111        | 41.67 | 3500 | 0.2638      