generated_from_trainer

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rhenus_v3.5

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

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 0.42 100 0.8800 0.0256 0.0010 0.0019 0.8497
No log 0.83 200 0.7576 0.0256 0.0010 0.0019 0.8497
No log 1.25 300 0.6665 0.1045 0.0141 0.0248 0.8539
No log 1.67 400 0.5845 0.2211 0.0673 0.1032 0.8669
0.7141 2.08 500 0.5312 0.2549 0.1166 0.16 0.8778
0.7141 2.5 600 0.4970 0.3097 0.1759 0.2244 0.8772
0.7141 2.92 700 0.4455 0.3603 0.2281 0.2794 0.8909
0.7141 3.33 800 0.3984 0.4536 0.3146 0.3715 0.9069
0.7141 3.75 900 0.3447 0.4848 0.3839 0.4285 0.9149
0.361 4.17 1000 0.3283 0.5557 0.5618 0.5587 0.9238
0.361 4.58 1100 0.3005 0.6024 0.5528 0.5765 0.9280
0.361 5.0 1200 0.2841 0.6866 0.6010 0.6409 0.9372
0.361 5.42 1300 0.2407 0.6731 0.6995 0.6861 0.9448
0.361 5.83 1400 0.2486 0.7316 0.6794 0.7045 0.9477
0.1955 6.25 1500 0.2110 0.7038 0.7045 0.7042 0.9490
0.1955 6.67 1600 0.2023 0.7075 0.7367 0.7218 0.9504
0.1955 7.08 1700 0.2137 0.6864 0.7457 0.7148 0.9454
0.1955 7.5 1800 0.2022 0.6903 0.7548 0.7211 0.9496
0.1955 7.92 1900 0.1818 0.7160 0.7678 0.7410 0.9562
0.1152 8.33 2000 0.1980 0.7132 0.7548 0.7334 0.9492
0.1152 8.75 2100 0.1930 0.7386 0.7638 0.7510 0.9519
0.1152 9.17 2200 0.1955 0.7333 0.7930 0.7620 0.9539
0.1152 9.58 2300 0.1815 0.7363 0.7719 0.7537 0.9572
0.1152 10.0 2400 0.1760 0.7905 0.8 0.7952 0.9627
0.0777 10.42 2500 0.1756 0.7532 0.7638 0.7585 0.9583
0.0777 10.83 2600 0.1901 0.7779 0.7920 0.7849 0.9595
0.0777 11.25 2700 0.1918 0.7371 0.7920 0.7636 0.9533
0.0777 11.67 2800 0.1744 0.8018 0.8090 0.8054 0.9644
0.0777 12.08 2900 0.1762 0.7805 0.8040 0.7921 0.9612
0.0562 12.5 3000 0.1774 0.7892 0.8090 0.7990 0.9595
0.0562 12.92 3100 0.1770 0.7722 0.8141 0.7926 0.9591
0.0562 13.33 3200 0.1712 0.8249 0.8191 0.8220 0.9661
0.0562 13.75 3300 0.1773 0.7909 0.8211 0.8057 0.9609
0.0562 14.17 3400 0.1964 0.7640 0.8201 0.7911 0.9594
0.0411 14.58 3500 0.1867 0.7797 0.8111 0.7951 0.9618
0.0411 15.0 3600 0.1850 0.7983 0.8271 0.8124 0.9610
0.0411 15.42 3700 0.1747 0.7948 0.8251 0.8097 0.9621
0.0411 15.83 3800 0.1861 0.7813 0.8332 0.8064 0.9588
0.0411 16.25 3900 0.1805 0.7935 0.8342 0.8133 0.9635
0.0313 16.67 4000 0.1910 0.7874 0.8261 0.8063 0.9609
0.0313 17.08 4100 0.1739 0.7856 0.8251 0.8049 0.9620
0.0313 17.5 4200 0.1828 0.7916 0.8362 0.8133 0.9621
0.0313 17.92 4300 0.1850 0.8239 0.8322 0.8280 0.9644
0.0313 18.33 4400 0.1771 0.8252 0.8352 0.8302 0.9644
0.0254 18.75 4500 0.1974 0.7971 0.8291 0.8128 0.9604
0.0254 19.17 4600 0.1865 0.8057 0.8291 0.8172 0.9627
0.0254 19.58 4700 0.1895 0.8197 0.8362 0.8279 0.9638
0.0254 20.0 4800 0.1781 0.8107 0.8352 0.8228 0.9642
0.0254 20.42 4900 0.1751 0.8303 0.8362 0.8332 0.9656
0.0212 20.83 5000 0.1823 0.8209 0.8382 0.8294 0.9650
0.0212 21.25 5100 0.1727 0.8246 0.8412 0.8328 0.9653
0.0212 21.67 5200 0.1758 0.7907 0.8392 0.8142 0.9627
0.0212 22.08 5300 0.1787 0.8104 0.8422 0.8260 0.9642
0.0212 22.5 5400 0.1816 0.8134 0.8412 0.8271 0.9639
0.0184 22.92 5500 0.1811 0.8134 0.8412 0.8271 0.9642

Framework versions