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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- generated_from_trainer |
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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-finetuned-invoice-2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlmv3-finetuned-invoice-2 |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1396 |
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- Precision: 0.7576 |
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- Recall: 0.8929 |
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- F1: 0.8197 |
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- Accuracy: 0.9742 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 4.35 | 100 | 0.4241 | 0.0 | 0.0 | 0.0 | 0.9135 | |
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| No log | 8.7 | 200 | 0.2990 | 0.2353 | 0.1429 | 0.1778 | 0.9239 | |
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| No log | 13.04 | 300 | 0.3107 | 0.5263 | 0.3571 | 0.4255 | 0.9458 | |
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| No log | 17.39 | 400 | 0.1345 | 0.6970 | 0.8214 | 0.7541 | 0.9742 | |
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| 0.2872 | 21.74 | 500 | 0.1396 | 0.7576 | 0.8929 | 0.8197 | 0.9742 | |
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| 0.2872 | 26.09 | 600 | 0.1673 | 0.8519 | 0.8214 | 0.8364 | 0.9690 | |
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| 0.2872 | 30.43 | 700 | 0.1784 | 0.8519 | 0.8214 | 0.8364 | 0.9690 | |
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| 0.2872 | 34.78 | 800 | 0.1401 | 0.7742 | 0.8571 | 0.8136 | 0.9729 | |
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| 0.2872 | 39.13 | 900 | 0.1480 | 0.7273 | 0.8571 | 0.7869 | 0.9716 | |
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| 0.0443 | 43.48 | 1000 | 0.1739 | 0.6970 | 0.8214 | 0.7541 | 0.9703 | |
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| 0.0443 | 47.83 | 1100 | 0.1786 | 0.7097 | 0.7857 | 0.7458 | 0.9690 | |
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| 0.0443 | 52.17 | 1200 | 0.1832 | 0.6970 | 0.8214 | 0.7541 | 0.9690 | |
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| 0.0443 | 56.52 | 1300 | 0.1861 | 0.6389 | 0.8214 | 0.7187 | 0.9690 | |
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| 0.0443 | 60.87 | 1400 | 0.2155 | 0.6667 | 0.7143 | 0.6897 | 0.9639 | |
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| 0.0198 | 65.22 | 1500 | 0.2087 | 0.6667 | 0.7143 | 0.6897 | 0.9652 | |
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| 0.0198 | 69.57 | 1600 | 0.1680 | 0.6970 | 0.8214 | 0.7541 | 0.9703 | |
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| 0.0198 | 73.91 | 1700 | 0.1664 | 0.6970 | 0.8214 | 0.7541 | 0.9703 | |
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| 0.0198 | 78.26 | 1800 | 0.1795 | 0.6970 | 0.8214 | 0.7541 | 0.9703 | |
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| 0.0198 | 82.61 | 1900 | 0.1807 | 0.6970 | 0.8214 | 0.7541 | 0.9703 | |
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| 0.0151 | 86.96 | 2000 | 0.1825 | 0.6970 | 0.8214 | 0.7541 | 0.9703 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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