generated_from_trainer

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rhenus_v4

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 0.36 100 0.5155 0.2749 0.1025 0.1493 0.8764
No log 0.73 200 0.4887 0.3016 0.1940 0.2361 0.8784
No log 1.09 300 0.4188 0.4023 0.2442 0.3039 0.8965
No log 1.45 400 0.3713 0.5075 0.4734 0.4899 0.9133
0.4041 1.82 500 0.3178 0.5643 0.4633 0.5088 0.9221
0.4041 2.18 600 0.2931 0.6100 0.5266 0.5653 0.9329
0.4041 2.55 700 0.2746 0.6217 0.6111 0.6163 0.9335
0.4041 2.91 800 0.2538 0.6453 0.6382 0.6417 0.9376
0.4041 3.27 900 0.2418 0.6686 0.6814 0.6750 0.9439
0.2007 3.64 1000 0.2116 0.6740 0.7045 0.6889 0.9457
0.2007 4.0 1100 0.2053 0.7194 0.7085 0.7139 0.9522
0.2007 4.36 1200 0.1878 0.7141 0.7307 0.7223 0.9521
0.2007 4.73 1300 0.1829 0.7356 0.7437 0.7396 0.9566
0.2007 5.09 1400 0.1845 0.7412 0.7598 0.7504 0.9547
0.1141 5.45 1500 0.1669 0.7645 0.7668 0.7657 0.9585
0.1141 5.82 1600 0.1767 0.7552 0.7688 0.7620 0.9572
0.1141 6.18 1700 0.1645 0.7880 0.7809 0.7845 0.9632
0.1141 6.55 1800 0.1549 0.7776 0.7940 0.7857 0.9642
0.1141 6.91 1900 0.1784 0.7305 0.8090 0.7678 0.9557
0.0732 7.27 2000 0.1684 0.7569 0.7759 0.7663 0.9569
0.0732 7.64 2100 0.1488 0.7870 0.8171 0.8018 0.9661
0.0732 8.0 2200 0.1726 0.7959 0.8231 0.8093 0.9652
0.0732 8.36 2300 0.1494 0.7973 0.8221 0.8095 0.9677
0.0732 8.73 2400 0.1750 0.7903 0.8221 0.8059 0.9641
0.053 9.09 2500 0.1806 0.7990 0.8191 0.8089 0.9630
0.053 9.45 2600 0.1644 0.7857 0.8291 0.8068 0.9652
0.053 9.82 2700 0.1878 0.8066 0.8302 0.8182 0.9644
0.053 10.18 2800 0.1717 0.8129 0.8382 0.8253 0.9674
0.053 10.55 2900 0.1731 0.8186 0.8392 0.8288 0.9688
0.0366 10.91 3000 0.1634 0.8133 0.8362 0.8246 0.9679
0.0366 11.27 3100 0.1514 0.8135 0.8332 0.8232 0.9687
0.0366 11.64 3200 0.1924 0.8060 0.8352 0.8203 0.9644
0.0366 12.0 3300 0.1665 0.7923 0.8241 0.8079 0.9649
0.0366 12.36 3400 0.1670 0.8214 0.8271 0.8242 0.9670
0.0275 12.73 3500 0.1822 0.8262 0.8362 0.8312 0.9664
0.0275 13.09 3600 0.1857 0.8049 0.8332 0.8188 0.9664
0.0275 13.45 3700 0.1648 0.8200 0.8422 0.8309 0.9642
0.0275 13.82 3800 0.1902 0.8086 0.8322 0.8202 0.9656
0.0275 14.18 3900 0.1600 0.8064 0.8372 0.8215 0.9668
0.0226 14.55 4000 0.1600 0.8030 0.8482 0.8250 0.9680
0.0226 14.91 4100 0.1696 0.8038 0.8442 0.8235 0.9664
0.0226 15.27 4200 0.1835 0.8227 0.8442 0.8333 0.9684
0.0226 15.64 4300 0.1772 0.8225 0.8382 0.8303 0.9682
0.0226 16.0 4400 0.1833 0.8166 0.8412 0.8287 0.9676
0.0174 16.36 4500 0.1807 0.8268 0.8442 0.8354 0.9671
0.0174 16.73 4600 0.1640 0.8357 0.8432 0.8394 0.9679
0.0174 17.09 4700 0.1769 0.8139 0.8482 0.8307 0.9659
0.0174 17.45 4800 0.1538 0.8541 0.8533 0.8537 0.9719
0.0174 17.82 4900 0.1751 0.8176 0.8382 0.8278 0.9677
0.0143 18.18 5000 0.1625 0.8478 0.8513 0.8495 0.9688
0.0143 18.55 5100 0.1831 0.8419 0.8513 0.8466 0.9705
0.0143 18.91 5200 0.1751 0.8312 0.8462 0.8386 0.9676
0.0143 19.27 5300 0.1685 0.8279 0.8462 0.8370 0.9685
0.0143 19.64 5400 0.1730 0.8219 0.8533 0.8373 0.9668
0.0111 20.0 5500 0.1782 0.8315 0.8482 0.8398 0.9670
0.0111 20.36 5600 0.1788 0.8229 0.8452 0.8339 0.9680
0.0111 20.73 5700 0.1787 0.8191 0.8462 0.8324 0.9668
0.0111 21.09 5800 0.1836 0.8456 0.8533 0.8494 0.9685
0.0111 21.45 5900 0.1814 0.8411 0.8513 0.8462 0.9680
0.0094 21.82 6000 0.1812 0.8145 0.8472 0.8305 0.9653
0.0094 22.18 6100 0.1815 0.8346 0.8523 0.8434 0.9691
0.0094 22.55 6200 0.1746 0.8383 0.8492 0.8437 0.9688
0.0094 22.91 6300 0.1896 0.8425 0.8442 0.8434 0.9679
0.0094 23.27 6400 0.1808 0.8383 0.8442 0.8413 0.9687
0.0074 23.64 6500 0.1680 0.8366 0.8543 0.8454 0.9688
0.0074 24.0 6600 0.1788 0.8343 0.8452 0.8397 0.9677
0.0074 24.36 6700 0.1821 0.8347 0.8422 0.8384 0.9679
0.0074 24.73 6800 0.1759 0.8302 0.8452 0.8376 0.9676
0.0074 25.09 6900 0.1737 0.8310 0.8452 0.8381 0.9676
0.0068 25.45 7000 0.1745 0.8327 0.8452 0.8389 0.9679

Framework versions