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: 0.9457652303120356
      - name: Recall
        type: recall
        value: 0.9528443113772455
      - name: F1
        type: f1
        value: 0.9492915734526474
      - name: Accuracy
        type: accuracy
        value: 0.9490662139219015
  ---
  
  <!-- 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: 0.2296
  - Precision: 0.9458
  - Recall: 0.9528
  - F1: 0.9493
  - Accuracy: 0.9491
  
  ## 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  | 1.1659          | 0.6767    | 0.7552 | 0.7138 | 0.7738   |
  | 1.4723        | 3.12  | 500  | 0.6092          | 0.8320    | 0.8600 | 0.8458 | 0.8667   |
  | 1.4723        | 4.69  | 750  | 0.4107          | 0.8730    | 0.9004 | 0.8865 | 0.9045   |
  | 0.4246        | 6.25  | 1000 | 0.3370          | 0.9143    | 0.9259 | 0.9200 | 0.9270   |
  | 0.4246        | 7.81  | 1250 | 0.2909          | 0.9267    | 0.9371 | 0.9319 | 0.9372   |
  | 0.2225        | 9.38  | 1500 | 0.2571          | 0.9355    | 0.9439 | 0.9396 | 0.9414   |
  | 0.2225        | 10.94 | 1750 | 0.2547          | 0.9383    | 0.9454 | 0.9418 | 0.9431   |
  | 0.1514        | 12.5  | 2000 | 0.2412          | 0.9306    | 0.9431 | 0.9368 | 0.9435   |
  | 0.1514        | 14.06 | 2250 | 0.2329          | 0.9443    | 0.9513 | 0.9478 | 0.9478   |
  | 0.1168        | 15.62 | 2500 | 0.2296          | 0.9458    | 0.9528 | 0.9493 | 0.9491   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.10.2+cpu
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  