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: 0.9415247964470762
      - name: Recall
        type: recall
        value: 0.9520958083832335
      - name: F1
        type: f1
        value: 0.9467807964272422
      - name: Accuracy
        type: accuracy
        value: 0.9575551782682513
  ---
  
  <!-- 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: 0.2246
  - Precision: 0.9415
  - Recall: 0.9521
  - F1: 0.9468
  - Accuracy: 0.9576
  
  ## 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  | 1.0265          | 0.7630    | 0.8099 | 0.7858 | 0.8086   |
  | 1.4021        | 3.12  | 500  | 0.5804          | 0.8290    | 0.8638 | 0.8460 | 0.8718   |
  | 1.4021        | 4.69  | 750  | 0.3937          | 0.8882    | 0.9034 | 0.8957 | 0.9126   |
  | 0.4062        | 6.25  | 1000 | 0.3171          | 0.9137    | 0.9274 | 0.9205 | 0.9351   |
  | 0.4062        | 7.81  | 1250 | 0.2798          | 0.9332    | 0.9409 | 0.9370 | 0.9444   |
  | 0.2212        | 9.38  | 1500 | 0.2558          | 0.9277    | 0.9416 | 0.9346 | 0.9461   |
  | 0.2212        | 10.94 | 1750 | 0.2479          | 0.9335    | 0.9454 | 0.9394 | 0.9516   |
  | 0.1525        | 12.5  | 2000 | 0.2356          | 0.9444    | 0.9536 | 0.9490 | 0.9588   |
  | 0.1525        | 14.06 | 2250 | 0.2286          | 0.9365    | 0.9491 | 0.9428 | 0.9563   |
  | 0.1134        | 15.62 | 2500 | 0.2246          | 0.9415    | 0.9521 | 0.9468 | 0.9576   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  