readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: perioli_vgm_model
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: sroie
        type: sroie
        config: discharge
        split: test
        args: discharge
      metrics:
      - name: Precision
        type: precision
        value: 0.7142857142857143
      - name: Recall
        type: recall
        value: 0.6790123456790124
      - name: F1
        type: f1
        value: 0.6962025316455697
      - name: Accuracy
        type: accuracy
        value: 0.991396968455551
  ---
  
  <!-- 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. -->
  
  # perioli_vgm_model
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0743
  - Precision: 0.7143
  - Recall: 0.6790
  - F1: 0.6962
  - Accuracy: 0.9914
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 1500
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 2.5   | 100  | 0.0928          | 0.1935    | 0.0741 | 0.1071 | 0.9779   |
  | No log        | 5.0   | 200  | 0.0638          | 0.7465    | 0.6543 | 0.6974 | 0.9906   |
  | No log        | 7.5   | 300  | 0.0702          | 0.6023    | 0.6543 | 0.6272 | 0.9865   |
  | No log        | 10.0  | 400  | 0.0661          | 0.7237    | 0.6790 | 0.7006 | 0.9906   |
  | 0.0711        | 12.5  | 500  | 0.0622          | 0.7681    | 0.6543 | 0.7067 | 0.9910   |
  | 0.0711        | 15.0  | 600  | 0.0632          | 0.7945    | 0.7160 | 0.7532 | 0.9926   |
  | 0.0711        | 17.5  | 700  | 0.0800          | 0.7692    | 0.6173 | 0.6849 | 0.9902   |
  | 0.0711        | 20.0  | 800  | 0.0712          | 0.6892    | 0.6296 | 0.6581 | 0.9902   |
  | 0.0711        | 22.5  | 900  | 0.0709          | 0.7160    | 0.7160 | 0.7160 | 0.9914   |
  | 0.0036        | 25.0  | 1000 | 0.0746          | 0.6747    | 0.6914 | 0.6829 | 0.9902   |
  | 0.0036        | 27.5  | 1100 | 0.0692          | 0.7037    | 0.7037 | 0.7037 | 0.9918   |
  | 0.0036        | 30.0  | 1200 | 0.0711          | 0.7089    | 0.6914 | 0.7    | 0.9914   |
  | 0.0036        | 32.5  | 1300 | 0.0729          | 0.6962    | 0.6790 | 0.6875 | 0.9910   |
  | 0.0036        | 35.0  | 1400 | 0.0737          | 0.7143    | 0.6790 | 0.6962 | 0.9914   |
  | 0.0015        | 37.5  | 1500 | 0.0743          | 0.7143    | 0.6790 | 0.6962 | 0.9914   |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  