## LayoutLMv3-Fine-Tuning-Invoice Model

#### Model description
**LayoutLMv3-Fine-Tuning-Invoice Model** is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice dataset. For the fine-tuning, We used [Invoice Dataset] that includes 12 labels ('Other', 'ABN', 'BILLER', 'BILLER_ADDRESS', 'BILLER_POST_CODE', 'DUE_DATE', 'GST', 'INVOICE_DATE', 'INVOICE_NUMBER', 'SUBTOTAL', 'TOTAL', 'BILLER_ADDRESS').

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the invoice dataset.


It achieves the following results on the evaluation set:
- Loss: 0.005334
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0



## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-05
- train_batch_size: 2
- eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000

### Training results

| Training Loss |  Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 100 | 0.0878          | 0.968     | 0.9817 | 0.9748 | 0.9966   |
| No log        | 200 | 0.0241          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 300 | 0.0186          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 400 | 0.0184          | 0.9854    | 0.9574 | 0.9712 | 0.9956   |
| 0.110800      | 500 | 0.0016          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.110800      | 600 | 0.0015          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.110800      | 700 | 0.0014          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.110800      | 800 | 0.0013          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.110800      | 900 | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.004900      | 1000 | 0.0012          | 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