readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlm_coc_model
    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: 0.7948717948717948
      - name: Recall
        type: recall
        value: 0.7322834645669292
      - name: F1
        type: f1
        value: 0.7622950819672131
      - name: Accuracy
        type: accuracy
        value: 0.9943641264395982
  ---
  
  <!-- 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. -->
  
  # layoutlm_coc_model
  
  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: 0.0331
  - Precision: 0.7949
  - Recall: 0.7323
  - F1: 0.7623
  - Accuracy: 0.9944
  
  ## 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        | 1.33  | 100  | 0.0609          | 0.4576    | 0.4252 | 0.4408 | 0.9855   |
  | No log        | 2.67  | 200  | 0.0336          | 0.6697    | 0.5748 | 0.6186 | 0.9912   |
  | No log        | 4.0   | 300  | 0.0299          | 0.6957    | 0.6299 | 0.6612 | 0.9922   |
  | No log        | 5.33  | 400  | 0.0287          | 0.7311    | 0.6850 | 0.7073 | 0.9929   |
  | 0.064         | 6.67  | 500  | 0.0311          | 0.7863    | 0.7244 | 0.7541 | 0.9941   |
  | 0.064         | 8.0   | 600  | 0.0317          | 0.7419    | 0.7244 | 0.7331 | 0.9936   |
  | 0.064         | 9.33  | 700  | 0.0330          | 0.7297    | 0.6378 | 0.6807 | 0.9926   |
  | 0.064         | 10.67 | 800  | 0.0315          | 0.7692    | 0.7087 | 0.7377 | 0.9939   |
  | 0.064         | 12.0  | 900  | 0.0341          | 0.8148    | 0.6929 | 0.7489 | 0.9941   |
  | 0.0063        | 13.33 | 1000 | 0.0331          | 0.7949    | 0.7323 | 0.7623 | 0.9944   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  