readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: euro-NewLayout
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: sroie
        type: sroie
        config: discharge
        split: train
        args: discharge
      metrics:
      - name: Precision
        type: precision
        value: 0.8518518518518519
      - name: Recall
        type: recall
        value: 0.8518518518518519
      - name: F1
        type: f1
        value: 0.8518518518518519
      - name: Accuracy
        type: accuracy
        value: 0.9694656488549618
  ---
  
  <!-- 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. -->
  
  # euro-NewLayout
  
  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: 0.3532
  - Precision: 0.8519
  - Recall: 0.8519
  - F1: 0.8519
  - Accuracy: 0.9695
  
  ## 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        | 12.5  | 100  | 1.5280          | 0.1       | 0.0185 | 0.0312 | 0.6641   |
  | No log        | 25.0  | 200  | 0.9623          | 0.4545    | 0.4630 | 0.4587 | 0.8664   |
  | No log        | 37.5  | 300  | 0.7261          | 0.5185    | 0.5185 | 0.5185 | 0.8931   |
  | No log        | 50.0  | 400  | 0.5971          | 0.6038    | 0.5926 | 0.5981 | 0.9122   |
  | 1.3737        | 62.5  | 500  | 0.5082          | 0.6727    | 0.6852 | 0.6789 | 0.9389   |
  | 1.3737        | 75.0  | 600  | 0.4335          | 0.7679    | 0.7963 | 0.7818 | 0.9580   |
  | 1.3737        | 87.5  | 700  | 0.4216          | 0.7759    | 0.8333 | 0.8036 | 0.9542   |
  | 1.3737        | 100.0 | 800  | 0.3650          | 0.8519    | 0.8519 | 0.8519 | 0.9695   |
  | 1.3737        | 112.5 | 900  | 0.3518          | 0.8519    | 0.8519 | 0.8519 | 0.9695   |
  | 0.4273        | 125.0 | 1000 | 0.3532          | 0.8519    | 0.8519 | 0.8519 | 0.9695   |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  