readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - cord-layoutlmv3
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-cord_100
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: cord-layoutlmv3
        type: cord-layoutlmv3
        config: cord
        split: train
        args: cord
      metrics:
      - name: Precision
        type: precision
        value: 0.8719646799116998
      - name: Recall
        type: recall
        value: 0.8869760479041916
      - name: F1
        type: f1
        value: 0.8794063079777364
      - name: Accuracy
        type: accuracy
        value: 0.8790322580645161
  ---
  
  <!-- 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. -->
  
  # layoutlmv3-finetuned-cord_100
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7215
  - Precision: 0.8720
  - Recall: 0.8870
  - F1: 0.8794
  - Accuracy: 0.8790
  
  ## 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: 5
  - eval_batch_size: 5
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 2500
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 12.5  | 250  | 1.0892          | 0.7345    | 0.7867 | 0.7597 | 0.7806   |
  | 1.3039        | 25.0  | 500  | 0.7150          | 0.8054    | 0.8428 | 0.8237 | 0.8281   |
  | 1.3039        | 37.5  | 750  | 0.6320          | 0.8335    | 0.8615 | 0.8473 | 0.8540   |
  | 0.2171        | 50.0  | 1000 | 0.6427          | 0.8651    | 0.8832 | 0.8741 | 0.8722   |
  | 0.2171        | 62.5  | 1250 | 0.6640          | 0.8672    | 0.8847 | 0.8759 | 0.8765   |
  | 0.0654        | 75.0  | 1500 | 0.6758          | 0.8650    | 0.8825 | 0.8737 | 0.8731   |
  | 0.0654        | 87.5  | 1750 | 0.7028          | 0.8684    | 0.8840 | 0.8761 | 0.8765   |
  | 0.0338        | 100.0 | 2000 | 0.7252          | 0.8710    | 0.8847 | 0.8778 | 0.8769   |
  | 0.0338        | 112.5 | 2250 | 0.7227          | 0.8710    | 0.8847 | 0.8778 | 0.8778   |
  | 0.0257        | 125.0 | 2500 | 0.7215          | 0.8720    | 0.8870 | 0.8794 | 0.8790   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  