generated_from_trainer

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perioli_manifesti_v8.0

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

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 0.39 100 0.1908 0.6353 0.7029 0.6674 0.9427
No log 0.78 200 0.0525 0.8934 0.9269 0.9098 0.9869
No log 1.17 300 0.0416 0.9066 0.9332 0.9197 0.9886
No log 1.56 400 0.0330 0.9287 0.9523 0.9404 0.9917
0.1696 1.95 500 0.0280 0.9368 0.9500 0.9433 0.9922
0.1696 2.34 600 0.0336 0.9276 0.9566 0.9419 0.9918
0.1696 2.73 700 0.0287 0.9381 0.9597 0.9488 0.9928
0.1696 3.12 800 0.0231 0.9522 0.9664 0.9593 0.9945
0.1696 3.52 900 0.0239 0.9369 0.9640 0.9503 0.9934
0.026 3.91 1000 0.0203 0.9605 0.9703 0.9654 0.9953
0.026 4.3 1100 0.0234 0.9553 0.9699 0.9626 0.9950
0.026 4.69 1200 0.0230 0.9477 0.9703 0.9589 0.9944
0.026 5.08 1300 0.0253 0.9540 0.9722 0.9630 0.9952
0.026 5.47 1400 0.0222 0.9517 0.9695 0.9605 0.9947
0.0152 5.86 1500 0.0263 0.9474 0.9711 0.9591 0.9947
0.0152 6.25 1600 0.0252 0.9519 0.9672 0.9595 0.9946
0.0152 6.64 1700 0.0235 0.9553 0.9695 0.9624 0.9948
0.0152 7.03 1800 0.0207 0.9571 0.9676 0.9623 0.9949
0.0152 7.42 1900 0.0272 0.9526 0.9734 0.9629 0.9951
0.0098 7.81 2000 0.0240 0.9527 0.9691 0.9609 0.9949
0.0098 8.2 2100 0.0253 0.9505 0.9687 0.9595 0.9947
0.0098 8.59 2200 0.0267 0.9444 0.9695 0.9568 0.9945
0.0098 8.98 2300 0.0217 0.9562 0.9726 0.9643 0.9953
0.0098 9.38 2400 0.0223 0.9591 0.9722 0.9656 0.9953
0.0072 9.77 2500 0.0243 0.9569 0.9726 0.9647 0.9954
0.0072 10.16 2600 0.0252 0.9520 0.9699 0.9609 0.9948
0.0072 10.55 2700 0.0233 0.9591 0.9726 0.9658 0.9954
0.0072 10.94 2800 0.0268 0.9458 0.9695 0.9575 0.9944
0.0072 11.33 2900 0.0266 0.9533 0.9746 0.9639 0.9952
0.0053 11.72 3000 0.0257 0.9536 0.9722 0.9628 0.9953
0.0053 12.11 3100 0.0215 0.9618 0.9738 0.9678 0.9958
0.0053 12.5 3200 0.0236 0.9544 0.9734 0.9638 0.9953
0.0053 12.89 3300 0.0229 0.9617 0.9726 0.9672 0.9957
0.0053 13.28 3400 0.0257 0.9581 0.9738 0.9659 0.9956
0.004 13.67 3500 0.0233 0.9651 0.9734 0.9692 0.9959
0.004 14.06 3600 0.0268 0.9513 0.9695 0.9603 0.9948
0.004 14.45 3700 0.0290 0.9532 0.9707 0.9618 0.9950
0.004 14.84 3800 0.0279 0.9558 0.9722 0.9640 0.9953
0.004 15.23 3900 0.0308 0.9524 0.9707 0.9615 0.9948
0.0031 15.62 4000 0.0272 0.9580 0.9730 0.9655 0.9955
0.0031 16.02 4100 0.0272 0.9569 0.9711 0.9639 0.9953
0.0031 16.41 4200 0.0279 0.9577 0.9726 0.9651 0.9954
0.0031 16.8 4300 0.0290 0.9517 0.9707 0.9611 0.9949
0.0031 17.19 4400 0.0274 0.9584 0.9719 0.9651 0.9954
0.0022 17.58 4500 0.0326 0.9521 0.9711 0.9615 0.9949
0.0022 17.97 4600 0.0310 0.9554 0.9719 0.9636 0.9952
0.0022 18.36 4700 0.0285 0.9558 0.9715 0.9636 0.9953
0.0022 18.75 4800 0.0295 0.9566 0.9746 0.9655 0.9956
0.0022 19.14 4900 0.0280 0.9580 0.9730 0.9655 0.9955
0.0015 19.53 5000 0.0320 0.9499 0.9719 0.9608 0.9950
0.0015 19.92 5100 0.0271 0.9641 0.9762 0.9701 0.9961
0.0015 20.31 5200 0.0276 0.9618 0.9754 0.9686 0.9959
0.0015 20.7 5300 0.0322 0.9537 0.9734 0.9634 0.9953
0.0015 21.09 5400 0.0282 0.9570 0.9746 0.9657 0.9955
0.0012 21.48 5500 0.0318 0.9533 0.9742 0.9637 0.9953
0.0012 21.88 5600 0.0286 0.9577 0.9742 0.9659 0.9955
0.0012 22.27 5700 0.0304 0.9570 0.9746 0.9657 0.9955
0.0012 22.66 5800 0.0299 0.9566 0.9738 0.9651 0.9954
0.0012 23.05 5900 0.0300 0.9566 0.9746 0.9655 0.9955
0.0011 23.44 6000 0.0299 0.9566 0.9746 0.9655 0.9955

Framework versions