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perioli_manifesti_v9.5
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: 0.0162
- Precision: 0.9697
- Recall: 0.9766
- F1: 0.9731
- Accuracy: 0.9969
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 | 0.31 | 100 | 0.1412 | 0.7584 | 0.7194 | 0.7384 | 0.9622 |
No log | 0.62 | 200 | 0.0503 | 0.8732 | 0.8952 | 0.8841 | 0.9839 |
No log | 0.94 | 300 | 0.0422 | 0.8848 | 0.9304 | 0.9070 | 0.9880 |
No log | 1.25 | 400 | 0.0257 | 0.9388 | 0.9542 | 0.9464 | 0.9930 |
0.1473 | 1.56 | 500 | 0.0242 | 0.9162 | 0.9520 | 0.9338 | 0.9921 |
0.1473 | 1.88 | 600 | 0.0190 | 0.9552 | 0.9542 | 0.9547 | 0.9945 |
0.1473 | 2.19 | 700 | 0.0208 | 0.9440 | 0.9638 | 0.9538 | 0.9944 |
0.1473 | 2.5 | 800 | 0.0211 | 0.9343 | 0.9595 | 0.9467 | 0.9936 |
0.1473 | 2.81 | 900 | 0.0168 | 0.9565 | 0.9680 | 0.9622 | 0.9952 |
0.0268 | 3.12 | 1000 | 0.0224 | 0.9438 | 0.9673 | 0.9554 | 0.9946 |
0.0268 | 3.44 | 1100 | 0.0179 | 0.9495 | 0.9680 | 0.9587 | 0.9951 |
0.0268 | 3.75 | 1200 | 0.0169 | 0.9578 | 0.9751 | 0.9664 | 0.9959 |
0.0268 | 4.06 | 1300 | 0.0178 | 0.9567 | 0.9737 | 0.9651 | 0.9959 |
0.0268 | 4.38 | 1400 | 0.0178 | 0.9607 | 0.9716 | 0.9661 | 0.9960 |
0.0147 | 4.69 | 1500 | 0.0180 | 0.9648 | 0.9730 | 0.9689 | 0.9962 |
0.0147 | 5.0 | 1600 | 0.0173 | 0.9573 | 0.9719 | 0.9646 | 0.9958 |
0.0147 | 5.31 | 1700 | 0.0151 | 0.9638 | 0.9755 | 0.9696 | 0.9965 |
0.0147 | 5.62 | 1800 | 0.0154 | 0.9679 | 0.9741 | 0.9710 | 0.9965 |
0.0147 | 5.94 | 1900 | 0.0181 | 0.9672 | 0.9737 | 0.9704 | 0.9963 |
0.0105 | 6.25 | 2000 | 0.0179 | 0.9641 | 0.9723 | 0.9682 | 0.9961 |
0.0105 | 6.56 | 2100 | 0.0188 | 0.9586 | 0.9712 | 0.9649 | 0.9957 |
0.0105 | 6.88 | 2200 | 0.0163 | 0.9649 | 0.9780 | 0.9714 | 0.9967 |
0.0105 | 7.19 | 2300 | 0.0175 | 0.9639 | 0.9758 | 0.9698 | 0.9966 |
0.0105 | 7.5 | 2400 | 0.0180 | 0.9614 | 0.9744 | 0.9679 | 0.9963 |
0.0077 | 7.81 | 2500 | 0.0166 | 0.9659 | 0.9748 | 0.9703 | 0.9966 |
0.0077 | 8.12 | 2600 | 0.0158 | 0.9697 | 0.9762 | 0.9729 | 0.9968 |
0.0077 | 8.44 | 2700 | 0.0160 | 0.9703 | 0.9766 | 0.9734 | 0.9969 |
0.0077 | 8.75 | 2800 | 0.0163 | 0.9683 | 0.9751 | 0.9717 | 0.9967 |
0.0077 | 9.06 | 2900 | 0.0161 | 0.9697 | 0.9766 | 0.9731 | 0.9969 |
0.0064 | 9.38 | 3000 | 0.0162 | 0.9697 | 0.9766 | 0.9731 | 0.9969 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3