readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: BoL-Only
    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: 0.8018018018018018
      - name: Recall
        type: recall
        value: 0.7672413793103449
      - name: F1
        type: f1
        value: 0.7841409691629956
      - name: Accuracy
        type: accuracy
        value: 0.9314055144586416
  ---
  
  <!-- 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. -->
  
  # BoL-Only
  
  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: 0.5049
  - Precision: 0.8018
  - Recall: 0.7672
  - F1: 0.7841
  - Accuracy: 0.9314
  
  ## 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: 1000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.93  | 100  | 0.3901          | 0.6875    | 0.6638 | 0.6754 | 0.9139   |
  | No log        | 1.85  | 200  | 0.4194          | 0.7311    | 0.75   | 0.7404 | 0.9354   |
  | No log        | 2.78  | 300  | 0.4883          | 0.7946    | 0.7672 | 0.7807 | 0.9321   |
  | No log        | 3.7   | 400  | 0.4926          | 0.8053    | 0.7845 | 0.7948 | 0.9354   |
  | 0.0234        | 4.63  | 500  | 0.5112          | 0.7876    | 0.7672 | 0.7773 | 0.9334   |
  | 0.0234        | 5.56  | 600  | 0.5216          | 0.7667    | 0.7931 | 0.7797 | 0.9287   |
  | 0.0234        | 6.48  | 700  | 0.4927          | 0.7419    | 0.7931 | 0.7667 | 0.9280   |
  | 0.0234        | 7.41  | 800  | 0.4979          | 0.8091    | 0.7672 | 0.7876 | 0.9301   |
  | 0.0234        | 8.33  | 900  | 0.5044          | 0.8091    | 0.7672 | 0.7876 | 0.9301   |
  | 0.0005        | 9.26  | 1000 | 0.5049          | 0.8018    | 0.7672 | 0.7841 | 0.9314   |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  