readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - sroie
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlm_manifesto_bigdataset
    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: 0.9910554561717353
      - name: Recall
        type: recall
        value: 0.992831541218638
      - name: F1
        type: f1
        value: 0.991942703670546
      - name: Accuracy
        type: accuracy
        value: 0.9988607234406152
  ---
  
  <!-- 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. -->
  
  # layoutlm_manifesto_bigdataset
  
  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: 0.0080
  - Precision: 0.9911
  - Recall: 0.9928
  - F1: 0.9919
  - Accuracy: 0.9989
  
  ## 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: 1500
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 2.0   | 100  | 0.0820          | 0.9071    | 0.9104 | 0.9088 | 0.9855   |
  | No log        | 4.0   | 200  | 0.0291          | 0.9441    | 0.9677 | 0.9558 | 0.9946   |
  | No log        | 6.0   | 300  | 0.0121          | 0.9751    | 0.9821 | 0.9786 | 0.9977   |
  | No log        | 8.0   | 400  | 0.0089          | 0.9911    | 0.9946 | 0.9928 | 0.9989   |
  | 0.1049        | 10.0  | 500  | 0.0083          | 0.9840    | 0.9892 | 0.9866 | 0.9983   |
  | 0.1049        | 12.0  | 600  | 0.0077          | 0.9875    | 0.9928 | 0.9902 | 0.9986   |
  | 0.1049        | 14.0  | 700  | 0.0081          | 0.9893    | 0.9910 | 0.9902 | 0.9986   |
  | 0.1049        | 16.0  | 800  | 0.0081          | 0.9875    | 0.9892 | 0.9884 | 0.9983   |
  | 0.1049        | 18.0  | 900  | 0.0081          | 0.9893    | 0.9910 | 0.9902 | 0.9986   |
  | 0.0051        | 20.0  | 1000 | 0.0074          | 0.9822    | 0.9875 | 0.9848 | 0.9980   |
  | 0.0051        | 22.0  | 1100 | 0.0083          | 0.9911    | 0.9928 | 0.9919 | 0.9989   |
  | 0.0051        | 24.0  | 1200 | 0.0073          | 0.9893    | 0.9910 | 0.9902 | 0.9986   |
  | 0.0051        | 26.0  | 1300 | 0.0070          | 0.9911    | 0.9928 | 0.9919 | 0.9989   |
  | 0.0051        | 28.0  | 1400 | 0.0081          | 0.9911    | 0.9928 | 0.9919 | 0.9989   |
  | 0.0022        | 30.0  | 1500 | 0.0080          | 0.9911    | 0.9928 | 0.9919 | 0.9989   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.2.2
  - Tokenizers 0.13.2
  