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_800
    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: 0.9445266272189349
      - name: Recall
        type: recall
        value: 0.9558383233532934
      - name: F1
        type: f1
        value: 0.9501488095238095
      - name: Accuracy
        type: accuracy
        value: 0.9605263157894737
  ---
  
  <!-- 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_800
  
  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: 0.2042
  - Precision: 0.9445
  - Recall: 0.9558
  - F1: 0.9501
  - Accuracy: 0.9605
  
  ## 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: 4000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.56  | 250  | 0.9737          | 0.7787    | 0.8166 | 0.7972 | 0.8188   |
  | 1.3706        | 3.12  | 500  | 0.5489          | 0.8480    | 0.8645 | 0.8562 | 0.8680   |
  | 1.3706        | 4.69  | 750  | 0.3857          | 0.8913    | 0.9087 | 0.8999 | 0.9147   |
  | 0.3693        | 6.25  | 1000 | 0.3192          | 0.9117    | 0.9274 | 0.9195 | 0.9317   |
  | 0.3693        | 7.81  | 1250 | 0.2816          | 0.9189    | 0.9326 | 0.9257 | 0.9355   |
  | 0.1903        | 9.38  | 1500 | 0.2521          | 0.9277    | 0.9409 | 0.9342 | 0.9465   |
  | 0.1903        | 10.94 | 1750 | 0.2353          | 0.9357    | 0.9476 | 0.9416 | 0.9550   |
  | 0.1231        | 12.5  | 2000 | 0.2361          | 0.9293    | 0.9446 | 0.9369 | 0.9516   |
  | 0.1231        | 14.06 | 2250 | 0.2194          | 0.9402    | 0.9528 | 0.9465 | 0.9576   |
  | 0.0766        | 15.62 | 2500 | 0.2133          | 0.9416    | 0.9528 | 0.9472 | 0.9580   |
  | 0.0766        | 17.19 | 2750 | 0.2117          | 0.9438    | 0.9558 | 0.9498 | 0.9597   |
  | 0.0585        | 18.75 | 3000 | 0.2152          | 0.9417    | 0.9551 | 0.9483 | 0.9605   |
  | 0.0585        | 20.31 | 3250 | 0.2070          | 0.9431    | 0.9551 | 0.9491 | 0.9588   |
  | 0.0454        | 21.88 | 3500 | 0.2093          | 0.9489    | 0.9588 | 0.9538 | 0.9622   |
  | 0.0454        | 23.44 | 3750 | 0.2034          | 0.9453    | 0.9566 | 0.9509 | 0.9610   |
  | 0.0409        | 25.0  | 4000 | 0.2042          | 0.9445    | 0.9558 | 0.9501 | 0.9605   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  