readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: BoL
    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: 0.6611570247933884
      - name: Recall
        type: recall
        value: 0.7339449541284404
      - name: F1
        type: f1
        value: 0.6956521739130436
      - name: Accuracy
        type: accuracy
        value: 0.9327804452204278
  ---
  
  <!-- 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
  
  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: 0.5739
  - Precision: 0.6612
  - Recall: 0.7339
  - F1: 0.6957
  - Accuracy: 0.9328
  
  ## 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        | 1.0   | 100  | 0.9945          | 0.375     | 0.0826 | 0.1353 | 0.8066   |
  | No log        | 2.0   | 200  | 0.5955          | 0.3022    | 0.3853 | 0.3387 | 0.9144   |
  | No log        | 3.0   | 300  | 0.5411          | 0.3852    | 0.4312 | 0.4069 | 0.9197   |
  | No log        | 4.0   | 400  | 0.5317          | 0.5210    | 0.5688 | 0.5439 | 0.9201   |
  | 0.6966        | 5.0   | 500  | 0.5550          | 0.5414    | 0.6606 | 0.5950 | 0.9319   |
  | 0.6966        | 6.0   | 600  | 0.4821          | 0.6694    | 0.7431 | 0.7043 | 0.9476   |
  | 0.6966        | 7.0   | 700  | 0.4974          | 0.6378    | 0.7431 | 0.6864 | 0.9441   |
  | 0.6966        | 8.0   | 800  | 0.4966          | 0.6641    | 0.7798 | 0.7173 | 0.9454   |
  | 0.6966        | 9.0   | 900  | 0.5670          | 0.6336    | 0.7615 | 0.6917 | 0.9337   |
  | 0.0362        | 10.0  | 1000 | 0.5739          | 0.6612    | 0.7339 | 0.6957 | 0.9328   |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  