readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3_perioli
    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: 0.9764705882352941
      - name: Recall
        type: recall
        value: 0.9613899613899614
      - name: F1
        type: f1
        value: 0.9688715953307393
      - name: Accuracy
        type: accuracy
        value: 0.9953795379537954
  ---
  
  <!-- 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_perioli
  
  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: 0.0371
  - Precision: 0.9765
  - Recall: 0.9614
  - F1: 0.9689
  - Accuracy: 0.9954
  
  ## 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: 500
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 2.94  | 50   | 0.1606          | 0.8352    | 0.8417 | 0.8385 | 0.9782   |
  | No log        | 5.88  | 100  | 0.0396          | 0.9565    | 0.9344 | 0.9453 | 0.9927   |
  | No log        | 8.82  | 150  | 0.0316          | 0.9608    | 0.9459 | 0.9533 | 0.9934   |
  | No log        | 11.76 | 200  | 0.0356          | 0.9881    | 0.9653 | 0.9766 | 0.9960   |
  | No log        | 14.71 | 250  | 0.0230          | 0.9882    | 0.9691 | 0.9786 | 0.9960   |
  | No log        | 17.65 | 300  | 0.0274          | 0.9881    | 0.9653 | 0.9766 | 0.9960   |
  | No log        | 20.59 | 350  | 0.0341          | 0.9881    | 0.9653 | 0.9766 | 0.9960   |
  | No log        | 23.53 | 400  | 0.0355          | 0.9765    | 0.9614 | 0.9689 | 0.9954   |
  | No log        | 26.47 | 450  | 0.0373          | 0.9765    | 0.9614 | 0.9689 | 0.9954   |
  | 0.0774        | 29.41 | 500  | 0.0371          | 0.9765    | 0.9614 | 0.9689 | 0.9954   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  