generated_from_trainer

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rhenus_v4.2

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 0.35 100 0.9405 0.0256 0.0010 0.0019 0.8497
No log 0.71 200 0.8105 0.0256 0.0010 0.0019 0.8497
No log 1.06 300 0.7300 0.0638 0.0030 0.0058 0.8498
No log 1.41 400 0.6387 0.1122 0.0231 0.0383 0.8519
0.7458 1.77 500 0.5767 0.1799 0.0774 0.1082 0.8608
0.7458 2.12 600 0.5390 0.2662 0.1407 0.1841 0.8737
0.7458 2.47 700 0.5101 0.3203 0.1729 0.2245 0.8739
0.7458 2.83 800 0.4334 0.4030 0.3216 0.3577 0.8936
0.7458 3.18 900 0.3992 0.4494 0.3879 0.4164 0.9026
0.3964 3.53 1000 0.3712 0.5261 0.4251 0.4703 0.9060
0.3964 3.89 1100 0.3243 0.5590 0.5286 0.5434 0.9241
0.3964 4.24 1200 0.2917 0.6143 0.5538 0.5825 0.9334
0.3964 4.59 1300 0.2765 0.5855 0.6231 0.6037 0.9335
0.3964 4.95 1400 0.2629 0.6499 0.6362 0.6430 0.9407
0.224 5.3 1500 0.2278 0.7106 0.6935 0.7019 0.9516
0.224 5.65 1600 0.2143 0.7209 0.7166 0.7187 0.9534
0.224 6.01 1700 0.2144 0.7280 0.7075 0.7176 0.9519
0.224 6.36 1800 0.1938 0.7513 0.7286 0.7398 0.9556
0.224 6.71 1900 0.2017 0.7309 0.7508 0.7407 0.9560
0.1325 7.07 2000 0.1873 0.7560 0.7628 0.7594 0.9586
0.1325 7.42 2100 0.2002 0.738 0.7417 0.7398 0.9566
0.1325 7.77 2200 0.1781 0.7186 0.7829 0.7494 0.9562
0.1325 8.13 2300 0.1747 0.7939 0.7899 0.7919 0.9629
0.1325 8.48 2400 0.1698 0.7528 0.8111 0.7808 0.9633
0.0862 8.83 2500 0.1615 0.8147 0.8040 0.8093 0.9676
0.0862 9.19 2600 0.1624 0.8078 0.7940 0.8008 0.9662
0.0862 9.54 2700 0.1615 0.8020 0.8020 0.8020 0.9629
0.0862 9.89 2800 0.1539 0.7765 0.8030 0.7895 0.9647
0.0862 10.25 2900 0.1465 0.8106 0.8171 0.8138 0.9679
0.062 10.6 3000 0.1462 0.8367 0.8291 0.8329 0.9709
0.062 10.95 3100 0.1477 0.7806 0.8261 0.8027 0.9677
0.062 11.31 3200 0.1535 0.8465 0.8201 0.8331 0.9708
0.062 11.66 3300 0.1470 0.8307 0.8332 0.8319 0.9722
0.062 12.01 3400 0.1402 0.8140 0.8402 0.8269 0.9706
0.046 12.37 3500 0.1637 0.8520 0.8161 0.8337 0.9712
0.046 12.72 3600 0.1426 0.8207 0.8281 0.8244 0.9699
0.046 13.07 3700 0.1578 0.8431 0.8261 0.8345 0.9699
0.046 13.43 3800 0.1508 0.8442 0.8332 0.8386 0.9722
0.046 13.78 3900 0.1427 0.8184 0.8332 0.8257 0.9706
0.0342 14.13 4000 0.1465 0.8135 0.8372 0.8252 0.9708
0.0342 14.49 4100 0.1402 0.8292 0.8342 0.8317 0.9728
0.0342 14.84 4200 0.1478 0.8325 0.8442 0.8383 0.9740
0.0342 15.19 4300 0.1440 0.8254 0.8412 0.8333 0.9728
0.0342 15.55 4400 0.1481 0.8333 0.8342 0.8338 0.9717
0.028 15.9 4500 0.1532 0.8494 0.8392 0.8443 0.9723
0.028 16.25 4600 0.1515 0.8098 0.8513 0.8300 0.9703
0.028 16.61 4700 0.1555 0.8271 0.8412 0.8341 0.9722
0.028 16.96 4800 0.1458 0.8465 0.8482 0.8474 0.9749
0.028 17.31 4900 0.1572 0.8373 0.8432 0.8403 0.9715
0.0206 17.67 5000 0.1621 0.8187 0.8442 0.8313 0.9706
0.0206 18.02 5100 0.1527 0.8318 0.8452 0.8385 0.9731
0.0206 18.37 5200 0.1533 0.8291 0.8482 0.8385 0.9725
0.0206 18.73 5300 0.1596 0.8265 0.8472 0.8367 0.9711
0.0206 19.08 5400 0.1549 0.844 0.8482 0.8461 0.9734
0.0184 19.43 5500 0.1594 0.8263 0.8462 0.8361 0.9714
0.0184 19.79 5600 0.1660 0.8267 0.8392 0.8329 0.9702
0.0184 20.14 5700 0.1578 0.8174 0.8503 0.8335 0.9722
0.0184 20.49 5800 0.1598 0.8222 0.8412 0.8316 0.9714
0.0184 20.85 5900 0.1601 0.8380 0.8372 0.8376 0.9729
0.0157 21.2 6000 0.1584 0.8313 0.8422 0.8367 0.9719
0.0157 21.55 6100 0.1593 0.84 0.8442 0.8421 0.9719
0.0157 21.91 6200 0.1616 0.8292 0.8492 0.8391 0.9726
0.0157 22.26 6300 0.1641 0.8314 0.8472 0.8392 0.9717
0.0157 22.61 6400 0.1581 0.8385 0.8503 0.8443 0.9735
0.0129 22.97 6500 0.1592 0.8385 0.8503 0.8443 0.9737
0.0129 23.32 6600 0.1625 0.8327 0.8503 0.8414 0.9723
0.0129 23.67 6700 0.1613 0.8347 0.8472 0.8409 0.9723
0.0129 24.03 6800 0.1616 0.8335 0.8452 0.8393 0.9729
0.0129 24.38 6900 0.1611 0.8343 0.8452 0.8397 0.9731
0.0115 24.73 7000 0.1615 0.8368 0.8452 0.8410 0.9731

Framework versions