readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - cord
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-cord_100
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: cord
        type: cord
        args: cord
      metrics:
      - name: Precision
        type: precision
        value: 0.9174649963154016
      - name: Recall
        type: recall
        value: 0.9318862275449101
      - name: F1
        type: f1
        value: 0.9246193835870776
      - name: Accuracy
        type: accuracy
        value: 0.9405772495755518
  ---
  
  <!-- 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 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2834
  - Precision: 0.9175
  - Recall: 0.9319
  - F1: 0.9246
  - Accuracy: 0.9406
  
  ## 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        | 4.17  | 250  | 1.0175          | 0.7358    | 0.7882 | 0.7611 | 0.8014   |
  | 1.406         | 8.33  | 500  | 0.5646          | 0.8444    | 0.8735 | 0.8587 | 0.8671   |
  | 1.406         | 12.5  | 750  | 0.3943          | 0.8950    | 0.9184 | 0.9065 | 0.9189   |
  | 0.3467        | 16.67 | 1000 | 0.3379          | 0.9138    | 0.9289 | 0.9213 | 0.9291   |
  | 0.3467        | 20.83 | 1250 | 0.2842          | 0.9189    | 0.9334 | 0.9261 | 0.9419   |
  | 0.1484        | 25.0  | 1500 | 0.2822          | 0.9233    | 0.9371 | 0.9302 | 0.9427   |
  | 0.1484        | 29.17 | 1750 | 0.2906          | 0.9168    | 0.9319 | 0.9243 | 0.9372   |
  | 0.0825        | 33.33 | 2000 | 0.2922          | 0.9183    | 0.9334 | 0.9258 | 0.9410   |
  | 0.0825        | 37.5  | 2250 | 0.2842          | 0.9154    | 0.9319 | 0.9236 | 0.9397   |
  | 0.0596        | 41.67 | 2500 | 0.2834          | 0.9175    | 0.9319 | 0.9246 | 0.9406   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  