| | --- |
| | 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 |
| | |
