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_500
    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: 0.9509293680297398
      - name: Recall
        type: recall
        value: 0.9573353293413174
      - name: F1
        type: f1
        value: 0.9541215964192465
      - name: Accuracy
        type: accuracy
        value: 0.9609507640067911
  ---
  
  <!-- 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_500
  
  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: 0.2339
  - Precision: 0.9509
  - Recall: 0.9573
  - F1: 0.9541
  - Accuracy: 0.9610
  
  ## 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: 4000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 2.5   | 250  | 0.9950          | 0.7114    | 0.7784 | 0.7434 | 0.7903   |
  | 1.3831        | 5.0   | 500  | 0.5152          | 0.8483    | 0.8787 | 0.8632 | 0.8816   |
  | 1.3831        | 7.5   | 750  | 0.3683          | 0.9013    | 0.9154 | 0.9083 | 0.9240   |
  | 0.3551        | 10.0  | 1000 | 0.3051          | 0.9201    | 0.9304 | 0.9252 | 0.9363   |
  | 0.3551        | 12.5  | 1250 | 0.2636          | 0.9375    | 0.9424 | 0.9399 | 0.9457   |
  | 0.1562        | 15.0  | 1500 | 0.2498          | 0.9385    | 0.9476 | 0.9430 | 0.9508   |
  | 0.1562        | 17.5  | 1750 | 0.2380          | 0.9414    | 0.9499 | 0.9456 | 0.9559   |
  | 0.0863        | 20.0  | 2000 | 0.2355          | 0.9400    | 0.9491 | 0.9445 | 0.9542   |
  | 0.0863        | 22.5  | 2250 | 0.2268          | 0.9451    | 0.9536 | 0.9493 | 0.9601   |
  | 0.0512        | 25.0  | 2500 | 0.2277          | 0.9429    | 0.9513 | 0.9471 | 0.9588   |
  | 0.0512        | 27.5  | 2750 | 0.2315          | 0.9473    | 0.9551 | 0.9512 | 0.9593   |
  | 0.0358        | 30.0  | 3000 | 0.2294          | 0.9509    | 0.9573 | 0.9541 | 0.9605   |
  | 0.0358        | 32.5  | 3250 | 0.2330          | 0.9458    | 0.9543 | 0.9501 | 0.9593   |
  | 0.028         | 35.0  | 3500 | 0.2374          | 0.9487    | 0.9558 | 0.9523 | 0.9597   |
  | 0.028         | 37.5  | 3750 | 0.2374          | 0.9501    | 0.9558 | 0.9530 | 0.9593   |
  | 0.0244        | 40.0  | 4000 | 0.2339          | 0.9509    | 0.9573 | 0.9541 | 0.9610   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  