readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - drug_bill_layoutv3
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-cord_100
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: drug_bill_layoutv3
        type: drug_bill_layoutv3
        config: Vin_Drug_Bill
        split: train
        args: Vin_Drug_Bill
      metrics:
      - name: Precision
        type: precision
        value: 1.0
      - name: Recall
        type: recall
        value: 1.0
      - name: F1
        type: f1
        value: 1.0
      - name: Accuracy
        type: accuracy
        value: 1.0
  ---
  
  <!-- 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 drug_bill_layoutv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0003
  - Precision: 1.0
  - Recall: 1.0
  - F1: 1.0
  - Accuracy: 1.0
  
  ## 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        | 1.33  | 250  | 0.0010          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0648        | 2.66  | 500  | 0.0003          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0648        | 3.99  | 750  | 0.0004          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0115        | 5.32  | 1000 | 0.0002          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0115        | 6.65  | 1250 | 0.0002          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0134        | 7.98  | 1500 | 0.0001          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0134        | 9.31  | 1750 | 0.0001          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0045        | 10.64 | 2000 | 0.0001          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0045        | 11.97 | 2250 | 0.0001          | 1.0       | 1.0    | 1.0 | 1.0      |
  | 0.0004        | 13.3  | 2500 | 0.0001          | 1.0       | 1.0    | 1.0 | 1.0      |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  