readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - cord-layoutlmv
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-cord_100
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: cord-layoutlmv
        type: cord-layoutlmv
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.8383838383838383
      - name: Recall
        type: recall
        value: 0.8877005347593583
      - name: F1
        type: f1
        value: 0.8623376623376623
      - name: Accuracy
        type: accuracy
        value: 0.9755271084337349
  ---
  
  <!-- 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-layoutlmv dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1524
  - Precision: 0.8384
  - Recall: 0.8877
  - F1: 0.8623
  - Accuracy: 0.9755
  
  ## 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: 500
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 27.78 | 250  | 0.2430          | 0.7526    | 0.7807 | 0.7664 | 0.9518   |
  | 0.4695        | 55.56 | 500  | 0.1524          | 0.8384    | 0.8877 | 0.8623 | 0.9755   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  