readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - invoices
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: invoices
        type: invoices
        config: sroie
        split: train
        args: sroie
      metrics:
      - name: Precision
        type: precision
        value: 0.975
      - name: Recall
        type: recall
        value: 0.975
      - name: F1
        type: f1
        value: 0.975
      - name: Accuracy
        type: accuracy
        value: 0.975
  ---
  
  <!-- 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. -->
  
  # layoutlmv3-finetuned-invoice
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoices dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2299
  - Precision: 0.975
  - Recall: 0.975
  - F1: 0.975
  - Accuracy: 0.975
  
  ## 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: 2000
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1    | Accuracy |
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:-----:|:--------:|
  | No log        | 14.29  | 100  | 0.1616          | 0.975     | 0.975  | 0.975 | 0.975    |
  | No log        | 28.57  | 200  | 0.1909          | 0.975     | 0.975  | 0.975 | 0.975    |
  | No log        | 42.86  | 300  | 0.2046          | 0.975     | 0.975  | 0.975 | 0.975    |
  | No log        | 57.14  | 400  | 0.2134          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.1239        | 71.43  | 500  | 0.2299          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.1239        | 85.71  | 600  | 0.2309          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.1239        | 100.0  | 700  | 0.2342          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.1239        | 114.29 | 800  | 0.2407          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.1239        | 128.57 | 900  | 0.2428          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0007        | 142.86 | 1000 | 0.2449          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0007        | 157.14 | 1100 | 0.2465          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0007        | 171.43 | 1200 | 0.2488          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0007        | 185.71 | 1300 | 0.2515          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0007        | 200.0  | 1400 | 0.2525          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0004        | 214.29 | 1500 | 0.2540          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0004        | 228.57 | 1600 | 0.2557          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0004        | 242.86 | 1700 | 0.2564          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0004        | 257.14 | 1800 | 0.2570          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0004        | 271.43 | 1900 | 0.2573          | 0.975     | 0.975  | 0.975 | 0.975    |
  | 0.0003        | 285.71 | 2000 | 0.2574          | 0.975     | 0.975  | 0.975 | 0.975    |
  
  
  ### Framework versions
  
  - Transformers 4.23.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  