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perioli_manifesti_v5.8.5
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: 0.0265
- Precision: 0.9645
- Recall: 0.9685
- F1: 0.9665
- Accuracy: 0.9953
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: 2500
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 0.4 | 100 | 0.1218 | 0.8262 | 0.7523 | 0.7875 | 0.9728 |
No log | 0.81 | 200 | 0.0500 | 0.8819 | 0.9200 | 0.9005 | 0.9880 |
No log | 1.21 | 300 | 0.0346 | 0.9326 | 0.9451 | 0.9388 | 0.9923 |
No log | 1.62 | 400 | 0.0307 | 0.9467 | 0.9539 | 0.9502 | 0.9937 |
0.1438 | 2.02 | 500 | 0.0280 | 0.9479 | 0.9574 | 0.9526 | 0.9941 |
0.1438 | 2.43 | 600 | 0.0334 | 0.9307 | 0.9486 | 0.9395 | 0.9924 |
0.1438 | 2.83 | 700 | 0.0312 | 0.9469 | 0.9585 | 0.9527 | 0.9938 |
0.1438 | 3.24 | 800 | 0.0288 | 0.9520 | 0.9614 | 0.9567 | 0.9943 |
0.1438 | 3.64 | 900 | 0.0257 | 0.9527 | 0.9638 | 0.9582 | 0.9945 |
0.0169 | 4.05 | 1000 | 0.0249 | 0.9505 | 0.9650 | 0.9577 | 0.9945 |
0.0169 | 4.45 | 1100 | 0.0245 | 0.9548 | 0.9632 | 0.9590 | 0.9945 |
0.0169 | 4.86 | 1200 | 0.0272 | 0.9571 | 0.9638 | 0.9604 | 0.9948 |
0.0169 | 5.26 | 1300 | 0.0287 | 0.9509 | 0.9620 | 0.9564 | 0.9943 |
0.0169 | 5.67 | 1400 | 0.0264 | 0.9605 | 0.9655 | 0.9630 | 0.9951 |
0.0108 | 6.07 | 1500 | 0.0276 | 0.9617 | 0.9667 | 0.9642 | 0.9951 |
0.0108 | 6.48 | 1600 | 0.0282 | 0.9677 | 0.9632 | 0.9655 | 0.9950 |
0.0108 | 6.88 | 1700 | 0.0253 | 0.9617 | 0.9673 | 0.9645 | 0.9954 |
0.0108 | 7.29 | 1800 | 0.0275 | 0.9600 | 0.9661 | 0.9630 | 0.9949 |
0.0108 | 7.69 | 1900 | 0.0257 | 0.9616 | 0.9661 | 0.9639 | 0.9951 |
0.0066 | 8.1 | 2000 | 0.0247 | 0.9662 | 0.9673 | 0.9667 | 0.9953 |
0.0066 | 8.5 | 2100 | 0.0262 | 0.9584 | 0.9679 | 0.9631 | 0.9950 |
0.0066 | 8.91 | 2200 | 0.0257 | 0.9634 | 0.9696 | 0.9665 | 0.9953 |
0.0066 | 9.31 | 2300 | 0.0281 | 0.9606 | 0.9690 | 0.9648 | 0.9951 |
0.0066 | 9.72 | 2400 | 0.0263 | 0.9645 | 0.9685 | 0.9665 | 0.9953 |
0.0048 | 10.12 | 2500 | 0.0265 | 0.9645 | 0.9685 | 0.9665 | 0.9953 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3