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: 0.917960088691796
      - name: Recall
        type: recall
        value: 0.9296407185628742
      - name: F1
        type: f1
        value: 0.9237634808478989
      - name: Accuracy
        type: accuracy
        value: 0.9303904923599321
  ---
  
  <!-- 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: 0.2854
  - Precision: 0.9180
  - Recall: 0.9296
  - F1: 0.9238
  - Accuracy: 0.9304
  
  ## 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: 2
  - eval_batch_size: 2
  - 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        | 0.62  | 250  | 1.2967          | 0.6175    | 0.7021 | 0.6571 | 0.7296   |
  | 1.6872        | 1.25  | 500  | 0.7576          | 0.8140    | 0.8383 | 0.8260 | 0.8383   |
  | 1.6872        | 1.88  | 750  | 0.5695          | 0.8301    | 0.8518 | 0.8408 | 0.8544   |
  | 0.6109        | 2.5   | 1000 | 0.4778          | 0.8564    | 0.875  | 0.8656 | 0.8812   |
  | 0.6109        | 3.12  | 1250 | 0.3825          | 0.8694    | 0.8922 | 0.8807 | 0.8986   |
  | 0.3905        | 3.75  | 1500 | 0.3546          | 0.8831    | 0.9049 | 0.8939 | 0.9143   |
  | 0.3905        | 4.38  | 1750 | 0.3153          | 0.8998    | 0.9207 | 0.9101 | 0.9223   |
  | 0.275         | 5.0   | 2000 | 0.3065          | 0.8926    | 0.9147 | 0.9035 | 0.9202   |
  | 0.275         | 5.62  | 2250 | 0.2872          | 0.9131    | 0.9281 | 0.9206 | 0.9291   |
  | 0.2275        | 6.25  | 2500 | 0.2854          | 0.9180    | 0.9296 | 0.9238 | 0.9304   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cpu
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  