generated_from_trainer

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perioli_manifesti_v5.8.4

This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.4 100 0.1199 0.8069 0.7965 0.8016 0.9730
No log 0.81 200 0.0519 0.8943 0.9205 0.9072 0.9887
No log 1.21 300 0.0353 0.9284 0.9409 0.9347 0.9915
No log 1.62 400 0.0344 0.9333 0.9497 0.9414 0.9921
0.1473 2.02 500 0.0261 0.9510 0.9649 0.9579 0.9942
0.1473 2.43 600 0.0266 0.9566 0.9667 0.9616 0.9945
0.1473 2.83 700 0.0250 0.9674 0.9719 0.9697 0.9955
0.1473 3.24 800 0.0278 0.9521 0.9637 0.9579 0.9942
0.1473 3.64 900 0.0270 0.9566 0.9661 0.9613 0.9947
0.0178 4.05 1000 0.0263 0.9649 0.9637 0.9643 0.9949
0.0178 4.45 1100 0.0232 0.9559 0.9637 0.9598 0.9946
0.0178 4.86 1200 0.0233 0.9662 0.9684 0.9673 0.9955
0.0178 5.26 1300 0.0241 0.9685 0.9696 0.9690 0.9958
0.0178 5.67 1400 0.0258 0.9629 0.9702 0.9665 0.9953
0.0103 6.07 1500 0.0269 0.9669 0.9725 0.9697 0.9954
0.0103 6.48 1600 0.0256 0.9683 0.9649 0.9666 0.9951
0.0103 6.88 1700 0.0276 0.9695 0.9673 0.9684 0.9955
0.0103 7.29 1800 0.0270 0.9656 0.9678 0.9667 0.9953
0.0103 7.69 1900 0.0280 0.9634 0.9684 0.9659 0.9953
0.0069 8.1 2000 0.0254 0.9612 0.9573 0.9593 0.9947
0.0069 8.5 2100 0.0257 0.9667 0.9684 0.9676 0.9953
0.0069 8.91 2200 0.0284 0.9662 0.9708 0.9685 0.9955
0.0069 9.31 2300 0.0252 0.9679 0.9708 0.9693 0.9958
0.0069 9.72 2400 0.0248 0.9709 0.9743 0.9726 0.9961
0.0056 10.12 2500 0.0278 0.9646 0.9725 0.9685 0.9955
0.0056 10.53 2600 0.0294 0.9662 0.9702 0.9682 0.9954
0.0056 10.93 2700 0.0298 0.96 0.9684 0.9642 0.9950
0.0056 11.34 2800 0.0297 0.9679 0.9690 0.9684 0.9954
0.0056 11.74 2900 0.0311 0.9634 0.9696 0.9665 0.9953
0.0037 12.15 3000 0.0302 0.9629 0.9702 0.9665 0.9953
0.0037 12.55 3100 0.0300 0.9599 0.9667 0.9633 0.9950
0.0037 12.96 3200 0.0293 0.9645 0.9684 0.9664 0.9952
0.0037 13.36 3300 0.0292 0.9646 0.9708 0.9676 0.9954
0.0037 13.77 3400 0.0308 0.9612 0.9696 0.9654 0.9952
0.0031 14.17 3500 0.0292 0.9634 0.9696 0.9665 0.9953
0.0031 14.57 3600 0.0290 0.9651 0.9702 0.9676 0.9953
0.0031 14.98 3700 0.0296 0.9652 0.9719 0.9685 0.9955
0.0031 15.38 3800 0.0298 0.9634 0.9696 0.9665 0.9953
0.0031 15.79 3900 0.0296 0.9663 0.9713 0.9688 0.9955
0.0023 16.19 4000 0.0296 0.9663 0.9713 0.9688 0.9955

Framework versions