readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: nexon_jan_2023
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: sroie
        type: sroie
        config: discharge
        split: test
        args: discharge
      metrics:
      - name: Precision
        type: precision
        value: 0.975609756097561
      - name: Recall
        type: recall
        value: 0.9302325581395349
      - name: F1
        type: f1
        value: 0.9523809523809524
      - name: Accuracy
        type: accuracy
        value: 0.9971428571428571
  ---
  
  <!-- 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. -->
  
  # nexon_jan_2023
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0380
  - Precision: 0.9756
  - Recall: 0.9302
  - F1: 0.9524
  - Accuracy: 0.9971
  
  ## 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: 1500
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 16.67  | 100  | 0.1998          | 0.6286    | 0.5116 | 0.5641 | 0.9571   |
  | No log        | 33.33  | 200  | 0.0616          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | No log        | 50.0   | 300  | 0.0439          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | No log        | 66.67  | 400  | 0.0404          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.1151        | 83.33  | 500  | 0.0389          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.1151        | 100.0  | 600  | 0.0380          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.1151        | 116.67 | 700  | 0.0378          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.1151        | 133.33 | 800  | 0.0379          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.1151        | 150.0  | 900  | 0.0378          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.009         | 166.67 | 1000 | 0.0378          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.009         | 183.33 | 1100 | 0.0378          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.009         | 200.0  | 1200 | 0.0379          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.009         | 216.67 | 1300 | 0.0379          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.009         | 233.33 | 1400 | 0.0379          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  | 0.0064        | 250.0  | 1500 | 0.0380          | 0.9756    | 0.9302 | 0.9524 | 0.9971   |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  