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_100
    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: 0.9472118959107807
      - name: Recall
        type: recall
        value: 0.9535928143712575
      - name: F1
        type: f1
        value: 0.9503916449086163
      - name: Accuracy
        type: accuracy
        value: 0.9562818336162988
  ---
  
  <!-- 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-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2152
  - Precision: 0.9472
  - Recall: 0.9536
  - F1: 0.9504
  - Accuracy: 0.9563
  
  ## 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        | 1.56  | 250  | 0.9909          | 0.7582    | 0.8099 | 0.7832 | 0.8128   |
  | 1.3653        | 3.12  | 500  | 0.5650          | 0.8392    | 0.8675 | 0.8531 | 0.8756   |
  | 1.3653        | 4.69  | 750  | 0.3851          | 0.8865    | 0.9177 | 0.9018 | 0.9181   |
  | 0.3744        | 6.25  | 1000 | 0.3104          | 0.9280    | 0.9364 | 0.9322 | 0.9380   |
  | 0.3744        | 7.81  | 1250 | 0.2778          | 0.9347    | 0.9424 | 0.9385 | 0.9440   |
  | 0.1955        | 9.38  | 1500 | 0.2316          | 0.9327    | 0.9446 | 0.9386 | 0.9440   |
  | 0.1955        | 10.94 | 1750 | 0.2461          | 0.9414    | 0.9491 | 0.9452 | 0.9533   |
  | 0.1349        | 12.5  | 2000 | 0.2316          | 0.9379    | 0.9491 | 0.9435 | 0.9478   |
  | 0.1349        | 14.06 | 2250 | 0.2227          | 0.9487    | 0.9551 | 0.9519 | 0.9533   |
  | 0.1024        | 15.62 | 2500 | 0.2152          | 0.9472    | 0.9536 | 0.9504 | 0.9563   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  