readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - invoice
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: Invoice
        type: invoice
        args: invoice
      metrics:
      - name: Precision
        type: precision
        value: 1.0
      - name: Recall
        type: recall
        value: 1.0
      - name: F1
        type: f1
        value: 1.0
      - name: Accuracy
        type: accuracy
        value: 1.0
  ---
  
  # LayoutLM-v3 model fine-tuned on invoice dataset
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice dataset.
  
  We use Microsoft’s LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds.
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0012
  - Precision: 1.0
  - Recall: 1.0
  - F1: 1.0
  - Accuracy: 1.0
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  All the training codes are available from the below GitHub link.
  
  https://github.com/Theivaprakasham/layoutlmv3
  
  
  The model can be evaluated at the HuggingFace Spaces link:
  
  https://huggingface.co/spaces/Theivaprakasham/layoutlmv3_invoice
  
  ## 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        | 2.0   | 100  | 0.0878          | 0.968     | 0.9817 | 0.9748 | 0.9966   |
  | No log        | 4.0   | 200  | 0.0241          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
  | No log        | 6.0   | 300  | 0.0186          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
  | No log        | 8.0   | 400  | 0.0184          | 0.9854    | 0.9574 | 0.9712 | 0.9956   |
  | 0.1308        | 10.0  | 500  | 0.0121          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
  | 0.1308        | 12.0  | 600  | 0.0076          | 0.9939    | 0.9878 | 0.9908 | 0.9987   |
  | 0.1308        | 14.0  | 700  | 0.0047          | 1.0       | 0.9959 | 0.9980 | 0.9996   |
  | 0.1308        | 16.0  | 800  | 0.0036          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
  | 0.1308        | 18.0  | 900  | 0.0045          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
  | 0.0069        | 20.0  | 1000 | 0.0043          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
  | 0.0069        | 22.0  | 1100 | 0.0016          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0069        | 24.0  | 1200 | 0.0015          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0069        | 26.0  | 1300 | 0.0014          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0069        | 28.0  | 1400 | 0.0013          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0026        | 30.0  | 1500 | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0026        | 32.0  | 1600 | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0026        | 34.0  | 1700 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0026        | 36.0  | 1800 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.0026        | 38.0  | 1900 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
  | 0.002         | 40.0  | 2000 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
  
  
  ### Framework versions
  
  - Transformers 4.20.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  