generated_from_trainer

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perioli_vgm_v7.4

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 0.38 100 0.0535 0.7775 0.8319 0.8038 0.9910
No log 0.75 200 0.0463 0.8455 0.8431 0.8443 0.9928
No log 1.13 300 0.0490 0.8326 0.8431 0.8379 0.9930
No log 1.5 400 0.0434 0.8128 0.8207 0.8167 0.9921
0.0043 1.88 500 0.0454 0.8443 0.8277 0.8359 0.9931
0.0043 2.26 600 0.0472 0.8363 0.8585 0.8473 0.9932
0.0043 2.63 700 0.0492 0.8479 0.8431 0.8455 0.9933
0.0043 3.01 800 0.0436 0.8549 0.8501 0.8525 0.9939
0.0043 3.38 900 0.0529 0.8636 0.8249 0.8438 0.9933
0.0034 3.76 1000 0.0370 0.8746 0.8501 0.8622 0.9942
0.0034 4.14 1100 0.0415 0.8926 0.8263 0.8582 0.9939
0.0034 4.51 1200 0.0418 0.8829 0.8557 0.8691 0.9941
0.0034 4.89 1300 0.0467 0.9032 0.8361 0.8684 0.9940
0.0034 5.26 1400 0.0427 0.8787 0.8824 0.8805 0.9947
0.0017 5.64 1500 0.0405 0.8911 0.8599 0.8753 0.9944
0.0017 6.02 1600 0.0401 0.8820 0.8585 0.8701 0.9942
0.0017 6.39 1700 0.0443 0.8755 0.8768 0.8761 0.9944
0.0017 6.77 1800 0.0469 0.8873 0.8487 0.8676 0.9941
0.0017 7.14 1900 0.0502 0.8434 0.8599 0.8516 0.9937
0.0015 7.52 2000 0.0526 0.8219 0.8529 0.8371 0.9928
0.0015 7.89 2100 0.0499 0.8236 0.8501 0.8367 0.9929
0.0015 8.27 2200 0.0415 0.8936 0.8473 0.8699 0.9944
0.0015 8.65 2300 0.0415 0.8579 0.8627 0.8603 0.9941
0.0015 9.02 2400 0.0432 0.8905 0.8543 0.8721 0.9947
0.0009 9.4 2500 0.0441 0.8682 0.8669 0.8676 0.9944
0.0009 9.77 2600 0.0434 0.8969 0.8529 0.8744 0.9946
0.0009 10.15 2700 0.0433 0.8943 0.8529 0.8731 0.9946
0.0009 10.53 2800 0.0442 0.8838 0.8627 0.8731 0.9945
0.0009 10.9 2900 0.0422 0.8703 0.8739 0.8721 0.9944
0.0005 11.28 3000 0.0464 0.8632 0.8571 0.8602 0.9939
0.0005 11.65 3100 0.0449 0.8584 0.8487 0.8535 0.9938
0.0005 12.03 3200 0.0395 0.8701 0.8627 0.8664 0.9943
0.0005 12.41 3300 0.0413 0.8874 0.8613 0.8742 0.9946
0.0005 12.78 3400 0.0448 0.9083 0.8739 0.8908 0.9950
0.0009 13.16 3500 0.0438 0.8550 0.8922 0.8732 0.9943
0.0009 13.53 3600 0.0364 0.8841 0.8655 0.8747 0.9947
0.0009 13.91 3700 0.0456 0.8741 0.8852 0.8796 0.9946
0.0009 14.29 3800 0.0461 0.8940 0.8627 0.8781 0.9947
0.0009 14.66 3900 0.0449 0.8830 0.8669 0.8749 0.9946
0.0003 15.04 4000 0.0491 0.8416 0.8782 0.8595 0.9938
0.0003 15.41 4100 0.0468 0.8827 0.8641 0.8733 0.9945
0.0003 15.79 4200 0.0508 0.8848 0.8389 0.8613 0.9940
0.0003 16.17 4300 0.0471 0.8878 0.8641 0.8758 0.9946
0.0003 16.54 4400 0.0459 0.8828 0.8866 0.8847 0.9949
0.0002 16.92 4500 0.0456 0.8830 0.8669 0.8749 0.9945
0.0002 17.29 4600 0.0465 0.8693 0.8291 0.8487 0.9938
0.0002 17.67 4700 0.0442 0.8603 0.8711 0.8657 0.9942
0.0002 18.05 4800 0.0454 0.8525 0.8824 0.8672 0.9943
0.0002 18.42 4900 0.0421 0.8922 0.8810 0.8865 0.9951
0.0004 18.8 5000 0.0418 0.8889 0.8627 0.8756 0.9945
0.0004 19.17 5100 0.0418 0.8773 0.8711 0.8742 0.9946
0.0004 19.55 5200 0.0415 0.8829 0.8768 0.8798 0.9948
0.0004 19.92 5300 0.0429 0.8790 0.8754 0.8772 0.9947
0.0004 20.3 5400 0.0411 0.8958 0.8669 0.8811 0.9950
0.0001 20.68 5500 0.0415 0.8848 0.8711 0.8779 0.9947
0.0001 21.05 5600 0.0418 0.8848 0.8711 0.8779 0.9947
0.0001 21.43 5700 0.0426 0.8794 0.8782 0.8788 0.9948
0.0001 21.8 5800 0.0434 0.8807 0.8894 0.8850 0.9950
0.0001 22.18 5900 0.0437 0.8786 0.8922 0.8853 0.9950
0.0001 22.56 6000 0.0436 0.8786 0.8922 0.8853 0.9950

Framework versions