readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: CUP
    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: 0.9674418604651163
      - name: Recall
        type: recall
        value: 0.9952153110047847
      - name: F1
        type: f1
        value: 0.981132075471698
      - name: Accuracy
        type: accuracy
        value: 0.9977973568281938
  ---
  
  <!-- 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. -->
  
  # CUP
  
  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: 0.0166
  - Precision: 0.9674
  - Recall: 0.9952
  - F1: 0.9811
  - Accuracy: 0.9978
  
  ## 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: 1000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.47  | 100  | 0.0258          | 0.8851    | 0.9952 | 0.9369 | 0.9905   |
  | No log        | 2.94  | 200  | 0.0183          | 0.92      | 0.9904 | 0.9539 | 0.9941   |
  | No log        | 4.41  | 300  | 0.0143          | 0.9674    | 0.9952 | 0.9811 | 0.9978   |
  | No log        | 5.88  | 400  | 0.0166          | 0.9498    | 0.9952 | 0.9720 | 0.9963   |
  | 0.0211        | 7.35  | 500  | 0.0179          | 0.9412    | 0.9952 | 0.9674 | 0.9956   |
  | 0.0211        | 8.82  | 600  | 0.0161          | 0.9674    | 0.9952 | 0.9811 | 0.9978   |
  | 0.0211        | 10.29 | 700  | 0.0168          | 0.9674    | 0.9952 | 0.9811 | 0.9978   |
  | 0.0211        | 11.76 | 800  | 0.0168          | 0.9674    | 0.9952 | 0.9811 | 0.9978   |
  | 0.0211        | 13.24 | 900  | 0.0170          | 0.9674    | 0.9952 | 0.9811 | 0.9978   |
  | 0.0003        | 14.71 | 1000 | 0.0166          | 0.9674    | 0.9952 | 0.9811 | 0.9978   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  