readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: eurocorporation-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: 0.7142857142857143
      - name: Recall
        type: recall
        value: 0.7936507936507936
      - name: F1
        type: f1
        value: 0.7518796992481204
      - name: Accuracy
        type: accuracy
        value: 0.9627507163323782
  ---
  
  <!-- 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. -->
  
  # eurocorporation-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: 0.2239
  - Precision: 0.7143
  - Recall: 0.7937
  - F1: 0.7519
  - Accuracy: 0.9628
  
  ## 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        | 5.0   | 100  | 1.0334          | 0.1071    | 0.0476 | 0.0659 | 0.7765   |
  | No log        | 10.0  | 200  | 0.5859          | 0.4348    | 0.4762 | 0.4545 | 0.8653   |
  | No log        | 15.0  | 300  | 0.3646          | 0.5833    | 0.6667 | 0.6222 | 0.9284   |
  | No log        | 20.0  | 400  | 0.2628          | 0.6986    | 0.8095 | 0.7500 | 0.9628   |
  | 0.5757        | 25.0  | 500  | 0.2240          | 0.7222    | 0.8254 | 0.7704 | 0.9656   |
  | 0.5757        | 30.0  | 600  | 0.2360          | 0.6986    | 0.8095 | 0.7500 | 0.9628   |
  | 0.5757        | 35.0  | 700  | 0.2348          | 0.6712    | 0.7778 | 0.7206 | 0.9570   |
  | 0.5757        | 40.0  | 800  | 0.2072          | 0.7391    | 0.8095 | 0.7727 | 0.9656   |
  | 0.5757        | 45.0  | 900  | 0.2262          | 0.7143    | 0.7937 | 0.7519 | 0.9628   |
  | 0.0449        | 50.0  | 1000 | 0.2239          | 0.7143    | 0.7937 | 0.7519 | 0.9628   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  