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passive_invoices_v2
This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset. It achieves the following results on the evaluation set:
- Loss: 0.0901
- Precision: 0.8948
- Recall: 0.8791
- F1: 0.8869
- Accuracy: 0.9808
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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: 3000
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 0.15 | 100 | 1.0334 | 0.3214 | 0.0118 | 0.0228 | 0.7802 |
No log | 0.29 | 200 | 0.6691 | 0.4022 | 0.3336 | 0.3647 | 0.8314 |
No log | 0.44 | 300 | 0.4943 | 0.5850 | 0.5537 | 0.5689 | 0.8905 |
No log | 0.59 | 400 | 0.3823 | 0.7330 | 0.6461 | 0.6868 | 0.9336 |
0.9058 | 0.73 | 500 | 0.2996 | 0.7676 | 0.7119 | 0.7387 | 0.9447 |
0.9058 | 0.88 | 600 | 0.2370 | 0.7837 | 0.7617 | 0.7726 | 0.9553 |
0.9058 | 1.03 | 700 | 0.1981 | 0.7979 | 0.7799 | 0.7888 | 0.9594 |
0.9058 | 1.17 | 800 | 0.1755 | 0.8458 | 0.8127 | 0.8289 | 0.9671 |
0.9058 | 1.32 | 900 | 0.1558 | 0.8487 | 0.8262 | 0.8373 | 0.9702 |
0.256 | 1.47 | 1000 | 0.1470 | 0.8636 | 0.8219 | 0.8422 | 0.9709 |
0.256 | 1.62 | 1100 | 0.1317 | 0.8613 | 0.8442 | 0.8526 | 0.9734 |
0.256 | 1.76 | 1200 | 0.1282 | 0.8716 | 0.8413 | 0.8562 | 0.9751 |
0.256 | 1.91 | 1300 | 0.1249 | 0.8635 | 0.8479 | 0.8556 | 0.9739 |
0.256 | 2.06 | 1400 | 0.1172 | 0.8706 | 0.8645 | 0.8675 | 0.9763 |
0.153 | 2.2 | 1500 | 0.1171 | 0.8705 | 0.8612 | 0.8658 | 0.9758 |
0.153 | 2.35 | 1600 | 0.1120 | 0.8852 | 0.8660 | 0.8755 | 0.9775 |
0.153 | 2.5 | 1700 | 0.1057 | 0.8764 | 0.8645 | 0.8704 | 0.9772 |
0.153 | 2.64 | 1800 | 0.1044 | 0.8762 | 0.8621 | 0.8691 | 0.9777 |
0.153 | 2.79 | 1900 | 0.1023 | 0.8791 | 0.8708 | 0.8749 | 0.9784 |
0.1265 | 2.94 | 2000 | 0.1024 | 0.8956 | 0.8723 | 0.8838 | 0.9795 |
0.1265 | 3.08 | 2100 | 0.0999 | 0.8896 | 0.8702 | 0.8798 | 0.9793 |
0.1265 | 3.23 | 2200 | 0.0971 | 0.8883 | 0.8748 | 0.8815 | 0.9798 |
0.1265 | 3.38 | 2300 | 0.0948 | 0.8911 | 0.8783 | 0.8846 | 0.9802 |
0.1265 | 3.52 | 2400 | 0.0909 | 0.8918 | 0.8793 | 0.8855 | 0.9804 |
0.1044 | 3.67 | 2500 | 0.0902 | 0.8937 | 0.8748 | 0.8841 | 0.9805 |
0.1044 | 3.82 | 2600 | 0.0915 | 0.894 | 0.8793 | 0.8866 | 0.9808 |
0.1044 | 3.96 | 2700 | 0.0930 | 0.8949 | 0.8745 | 0.8846 | 0.9807 |
0.1044 | 4.11 | 2800 | 0.0913 | 0.8952 | 0.8809 | 0.8880 | 0.9810 |
0.1044 | 4.26 | 2900 | 0.0900 | 0.8938 | 0.8809 | 0.8873 | 0.9810 |
0.0872 | 4.41 | 3000 | 0.0901 | 0.8948 | 0.8791 | 0.8869 | 0.9808 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3