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passive_invoices_v1.0
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: 0.0268
- Precision: 0.9632
- Recall: 0.9638
- F1: 0.9635
- Accuracy: 0.9951
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 | 0.2 | 100 | 0.8701 | 0.2241 | 0.1133 | 0.1506 | 0.7733 |
No log | 0.4 | 200 | 0.4681 | 0.5464 | 0.5379 | 0.5421 | 0.8760 |
No log | 0.61 | 300 | 0.2142 | 0.8207 | 0.8250 | 0.8229 | 0.9604 |
No log | 0.81 | 400 | 0.1339 | 0.8929 | 0.8907 | 0.8918 | 0.9786 |
0.5099 | 1.01 | 500 | 0.0942 | 0.8998 | 0.8953 | 0.8975 | 0.9821 |
0.5099 | 1.21 | 600 | 0.0738 | 0.9251 | 0.9251 | 0.9251 | 0.9848 |
0.5099 | 1.41 | 700 | 0.0622 | 0.9140 | 0.9090 | 0.9115 | 0.9854 |
0.5099 | 1.62 | 800 | 0.0534 | 0.9405 | 0.9405 | 0.9405 | 0.9899 |
0.5099 | 1.82 | 900 | 0.0525 | 0.9213 | 0.9251 | 0.9232 | 0.9874 |
0.0711 | 2.02 | 1000 | 0.0439 | 0.9477 | 0.9483 | 0.9480 | 0.9915 |
0.0711 | 2.22 | 1100 | 0.0413 | 0.9485 | 0.9517 | 0.9501 | 0.9916 |
0.0711 | 2.42 | 1200 | 0.0381 | 0.9486 | 0.9483 | 0.9484 | 0.9923 |
0.0711 | 2.63 | 1300 | 0.0407 | 0.9457 | 0.9495 | 0.9476 | 0.9912 |
0.0711 | 2.83 | 1400 | 0.0368 | 0.9535 | 0.9523 | 0.9529 | 0.9927 |
0.0422 | 3.03 | 1500 | 0.0355 | 0.9349 | 0.9334 | 0.9341 | 0.9912 |
0.0422 | 3.23 | 1600 | 0.0312 | 0.9540 | 0.9579 | 0.9560 | 0.9930 |
0.0422 | 3.43 | 1700 | 0.0324 | 0.9444 | 0.9418 | 0.9431 | 0.9921 |
0.0422 | 3.64 | 1800 | 0.0314 | 0.9500 | 0.9523 | 0.9511 | 0.9928 |
0.0422 | 3.84 | 1900 | 0.0296 | 0.9536 | 0.9545 | 0.9540 | 0.9936 |
0.0302 | 4.04 | 2000 | 0.0285 | 0.9604 | 0.9619 | 0.9612 | 0.9946 |
0.0302 | 4.24 | 2100 | 0.0286 | 0.9570 | 0.9570 | 0.9570 | 0.9936 |
0.0302 | 4.44 | 2200 | 0.0284 | 0.9610 | 0.9607 | 0.9608 | 0.9948 |
0.0302 | 4.65 | 2300 | 0.0282 | 0.9595 | 0.9604 | 0.9599 | 0.9947 |
0.0302 | 4.85 | 2400 | 0.0280 | 0.9483 | 0.9486 | 0.9484 | 0.9937 |
0.0271 | 5.05 | 2500 | 0.0273 | 0.9613 | 0.9619 | 0.9616 | 0.9949 |
0.0271 | 5.25 | 2600 | 0.0276 | 0.9619 | 0.9625 | 0.9622 | 0.9949 |
0.0271 | 5.45 | 2700 | 0.0271 | 0.9626 | 0.9631 | 0.9628 | 0.9950 |
0.0271 | 5.66 | 2800 | 0.0269 | 0.9632 | 0.9638 | 0.9635 | 0.9951 |
0.0271 | 5.86 | 2900 | 0.0268 | 0.9632 | 0.9638 | 0.9635 | 0.9951 |
0.0241 | 6.06 | 3000 | 0.0268 | 0.9632 | 0.9638 | 0.9635 | 0.9951 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3