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_200
    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: 0.9033923303834809
      - name: Recall
        type: recall
        value: 0.9169161676646707
      - name: F1
        type: f1
        value: 0.9101040118870729
      - name: Accuracy
        type: accuracy
        value: 0.9121392190152802
  ---
  
  <!-- 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_200
  
  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: 0.4529
  - Precision: 0.9034
  - Recall: 0.9169
  - F1: 0.9101
  - Accuracy: 0.9121
  
  ## 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: 3000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 6.25  | 250  | 1.0785          | 0.6815    | 0.7575 | 0.7175 | 0.7780   |
  | 1.3902        | 12.5  | 500  | 0.5871          | 0.8542    | 0.8683 | 0.8612 | 0.8604   |
  | 1.3902        | 18.75 | 750  | 0.4572          | 0.8728    | 0.8937 | 0.8831 | 0.8905   |
  | 0.298         | 25.0  | 1000 | 0.3947          | 0.8936    | 0.9117 | 0.9026 | 0.9092   |
  | 0.298         | 31.25 | 1250 | 0.3925          | 0.8982    | 0.9177 | 0.9078 | 0.9117   |
  | 0.1023        | 37.5  | 1500 | 0.4290          | 0.8908    | 0.9102 | 0.9004 | 0.9041   |
  | 0.1023        | 43.75 | 1750 | 0.4220          | 0.8980    | 0.9162 | 0.9070 | 0.9117   |
  | 0.0475        | 50.0  | 2000 | 0.4755          | 0.8944    | 0.9064 | 0.9004 | 0.8990   |
  | 0.0475        | 56.25 | 2250 | 0.4635          | 0.8992    | 0.9147 | 0.9069 | 0.9070   |
  | 0.0296        | 62.5  | 2500 | 0.4475          | 0.9019    | 0.9154 | 0.9086 | 0.9117   |
  | 0.0296        | 68.75 | 2750 | 0.4484          | 0.9004    | 0.9139 | 0.9071 | 0.9079   |
  | 0.0242        | 75.0  | 3000 | 0.4529          | 0.9034    | 0.9169 | 0.9101 | 0.9121   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  