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: 0.9022777369581191
      - name: Recall
        type: recall
        value: 0.9191616766467066
      - name: F1
        type: f1
        value: 0.9106414534668149
      - name: Accuracy
        type: accuracy
        value: 0.9202037351443124
  ---
  
  <!-- 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: 0.3848
  - Precision: 0.9023
  - Recall: 0.9192
  - F1: 0.9106
  - Accuracy: 0.9202
  
  ## 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        | 6.25  | 250  | 0.9576          | 0.7878    | 0.8196 | 0.8034 | 0.8166   |
  | 1.3167        | 12.5  | 500  | 0.5210          | 0.8536    | 0.8772 | 0.8653 | 0.8846   |
  | 1.3167        | 18.75 | 750  | 0.4077          | 0.8798    | 0.9042 | 0.8918 | 0.9113   |
  | 0.2603        | 25.0  | 1000 | 0.3943          | 0.8902    | 0.9102 | 0.9001 | 0.9147   |
  | 0.2603        | 31.25 | 1250 | 0.3691          | 0.8980    | 0.9162 | 0.9070 | 0.9194   |
  | 0.1009        | 37.5  | 1500 | 0.3496          | 0.9130    | 0.9274 | 0.9202 | 0.9266   |
  | 0.1009        | 43.75 | 1750 | 0.3700          | 0.9078    | 0.9214 | 0.9146 | 0.9266   |
  | 0.056         | 50.0  | 2000 | 0.3724          | 0.9065    | 0.9214 | 0.9139 | 0.9215   |
  | 0.056         | 56.25 | 2250 | 0.3773          | 0.9051    | 0.9207 | 0.9128 | 0.9202   |
  | 0.0413        | 62.5  | 2500 | 0.3848          | 0.9023    | 0.9192 | 0.9106 | 0.9202   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  