readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - cord
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-cord
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: cord
        type: cord
        args: cord
      metrics:
      - name: Precision
        type: precision
        value: 0.9619686800894854
      - name: Recall
        type: recall
        value: 0.9655688622754491
      - name: F1
        type: f1
        value: 0.9637654090399701
      - name: Accuracy
        type: accuracy
        value: 0.9681663837011885
  ---
  
  <!-- 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
  
  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: 0.1845
  - Precision: 0.9620
  - Recall: 0.9656
  - F1: 0.9638
  - Accuracy: 0.9682
  
  The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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        | 2.0   | 100  | 0.5257          | 0.8223    | 0.8555 | 0.8386 | 0.8710   |
  | No log        | 4.0   | 200  | 0.3200          | 0.9118    | 0.9281 | 0.9199 | 0.9317   |
  | No log        | 6.0   | 300  | 0.2449          | 0.9298    | 0.9424 | 0.9361 | 0.9465   |
  | No log        | 8.0   | 400  | 0.1923          | 0.9472    | 0.9536 | 0.9504 | 0.9597   |
  | 0.4328        | 10.0  | 500  | 0.1857          | 0.9591    | 0.9656 | 0.9623 | 0.9682   |
  | 0.4328        | 12.0  | 600  | 0.2073          | 0.9597    | 0.9618 | 0.9607 | 0.9656   |
  | 0.4328        | 14.0  | 700  | 0.1804          | 0.9634    | 0.9663 | 0.9649 | 0.9703   |
  | 0.4328        | 16.0  | 800  | 0.1882          | 0.9634    | 0.9648 | 0.9641 | 0.9665   |
  | 0.4328        | 18.0  | 900  | 0.1800          | 0.9619    | 0.9648 | 0.9634 | 0.9677   |
  | 0.0318        | 20.0  | 1000 | 0.1845          | 0.9620    | 0.9656 | 0.9638 | 0.9682   |
  
  
  ### Framework versions
  
  - Transformers 4.19.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  