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_300
    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: 0.9325426241660489
      - name: Recall
        type: recall
        value: 0.9416167664670658
      - name: F1
        type: f1
        value: 0.9370577281191806
      - name: Accuracy
        type: accuracy
        value: 0.9363327674023769
  ---
  
  <!-- 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_300
  
  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: 0.3434
  - Precision: 0.9325
  - Recall: 0.9416
  - F1: 0.9371
  - Accuracy: 0.9363
  
  ## 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        | 4.17  | 250  | 1.0379          | 0.7204    | 0.7829 | 0.7504 | 0.7941   |
  | 1.4162        | 8.33  | 500  | 0.5642          | 0.8462    | 0.8772 | 0.8614 | 0.8820   |
  | 1.4162        | 12.5  | 750  | 0.3836          | 0.9055    | 0.9184 | 0.9119 | 0.9206   |
  | 0.3211        | 16.67 | 1000 | 0.3209          | 0.9139    | 0.9296 | 0.9217 | 0.9334   |
  | 0.3211        | 20.83 | 1250 | 0.2962          | 0.9275    | 0.9386 | 0.9330 | 0.9435   |
  | 0.1191        | 25.0  | 1500 | 0.2979          | 0.9254    | 0.9379 | 0.9316 | 0.9402   |
  | 0.1191        | 29.17 | 1750 | 0.3079          | 0.9282    | 0.9386 | 0.9334 | 0.9355   |
  | 0.059         | 33.33 | 2000 | 0.3039          | 0.9232    | 0.9364 | 0.9298 | 0.9325   |
  | 0.059         | 37.5  | 2250 | 0.3254          | 0.9248    | 0.9386 | 0.9316 | 0.9355   |
  | 0.0342        | 41.67 | 2500 | 0.3404          | 0.9246    | 0.9364 | 0.9305 | 0.9334   |
  | 0.0342        | 45.83 | 2750 | 0.3386          | 0.9354    | 0.9431 | 0.9392 | 0.9355   |
  | 0.0226        | 50.0  | 3000 | 0.3274          | 0.9354    | 0.9431 | 0.9392 | 0.9359   |
  | 0.0226        | 54.17 | 3250 | 0.3282          | 0.9341    | 0.9446 | 0.9393 | 0.9393   |
  | 0.017         | 58.33 | 3500 | 0.3475          | 0.9319    | 0.9424 | 0.9371 | 0.9363   |
  | 0.017         | 62.5  | 3750 | 0.3367          | 0.9340    | 0.9431 | 0.9385 | 0.9372   |
  | 0.0145        | 66.67 | 4000 | 0.3434          | 0.9325    | 0.9416 | 0.9371 | 0.9363   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  