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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- invoice |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layoutlmv3-fine-tuning-invoice |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: Invoice |
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type: invoice |
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args: invoice |
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metrics: |
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- name: Precision |
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type: precision |
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value: 1.0 |
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- name: Recall |
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type: recall |
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value: 1.0 |
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- name: F1 |
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type: f1 |
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value: 1.0 |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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--- |
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## LayoutLMv3-Fine-Tuning-Invoice Model |
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#### Model description |
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**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](https://huggingface.co/datasets/darentang/generated) that includes 12 labels ('Other', 'ABN', 'BILLER', 'BILLER_ADDRESS', 'BILLER_POST_CODE', 'DUE_DATE', 'GST', 'INVOICE_DATE', 'INVOICE_NUMBER', 'SUBTOTAL', 'TOTAL', 'BILLER_ADDRESS'). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.005334 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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- Accuracy: 1.0 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1.5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- optimizer: epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- training_steps: 1000 |
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### Training results |
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| Training Loss | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 100 | 0.070030 | 0.972000 | 0.985801 | 0.978852 | 0.997051 | |
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| No log | 200 | 0.017637 | 0.972000 | 0.985801 | 0.978852 | 0.997051 | |
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| No log | 300 | 0.015573 | 0.972000 | 0.985801 | 0.978852 | 0.997051 | |
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| No log | 400 | 0.011000 | 0.973737 | 0.977688 | 0.978852 | 0.996419 | |
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| 0.110800 | 500 | 0.005334 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.110800 | 600 | 0.002994 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.110800 | 700 | 0.002330 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.110800 | 800 | 0.002188 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.110800 | 900 | 0.002105 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.004900 | 1000 | 0.002111 | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.20.1 |
