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: 0.9289415247964471
      - name: Recall
        type: recall
        value: 0.9393712574850299
      - name: F1
        type: f1
        value: 0.9341272794938594
      - name: Accuracy
        type: accuracy
        value: 0.9393039049235993
  ---
  
  <!-- 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: 0.3066
  - Precision: 0.9289
  - Recall: 0.9394
  - F1: 0.9341
  - Accuracy: 0.9393
  
  ## 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        | 4.17  | 250  | 0.9691          | 0.7365    | 0.7867 | 0.7608 | 0.7992   |
  | 1.3706        | 8.33  | 500  | 0.5325          | 0.8645    | 0.8885 | 0.8763 | 0.8858   |
  | 1.3706        | 12.5  | 750  | 0.3943          | 0.8939    | 0.9139 | 0.9038 | 0.9151   |
  | 0.3211        | 16.67 | 1000 | 0.3364          | 0.9209    | 0.9319 | 0.9263 | 0.9342   |
  | 0.3211        | 20.83 | 1250 | 0.3217          | 0.9246    | 0.9364 | 0.9305 | 0.9346   |
  | 0.1405        | 25.0  | 1500 | 0.3100          | 0.9296    | 0.9394 | 0.9345 | 0.9355   |
  | 0.1405        | 29.17 | 1750 | 0.3113          | 0.9275    | 0.9386 | 0.9330 | 0.9363   |
  | 0.076         | 33.33 | 2000 | 0.3183          | 0.9280    | 0.9364 | 0.9322 | 0.9351   |
  | 0.076         | 37.5  | 2250 | 0.3125          | 0.9211    | 0.9356 | 0.9283 | 0.9363   |
  | 0.0549        | 41.67 | 2500 | 0.3066          | 0.9289    | 0.9394 | 0.9341 | 0.9393   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  