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| 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 | |