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metadata
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: test
    results: []

test

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 5.0749

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:

  • learning_rate: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss
5.3171 0.2212 50 4.6375
4.4008 0.4425 100 4.0771
4.0457 0.6637 150 3.8669
3.8671 0.8850 200 3.6725
3.4191 1.1062 250 3.7154
3.181 1.3274 300 3.2224
3.1471 1.5487 350 3.1611
2.8849 1.7699 400 2.9149
2.6279 1.9912 450 2.7628
2.1833 2.2124 500 2.6845
1.9236 2.4336 550 2.5157
1.9385 2.6549 600 2.3477
1.8375 2.8761 650 2.5764
1.5994 3.0973 700 2.5796
1.4455 3.3186 750 2.4148
1.4008 3.5398 800 2.3462
1.4988 3.7611 850 2.0116
1.3286 3.9823 900 2.4790
1.0156 4.2035 950 2.6341
1.0546 4.4248 1000 2.9160
0.9135 4.6460 1050 3.3701
1.0544 4.8673 1100 2.3959
0.8423 5.0885 1150 2.8365
0.8101 5.3097 1200 2.7091
0.6854 5.5310 1250 3.1581
0.7012 5.7522 1300 3.2229
0.7611 5.9735 1350 2.8766
0.5144 6.1947 1400 3.1662
0.6242 6.4159 1450 3.4253
0.619 6.6372 1500 3.4169
0.4874 6.8584 1550 3.6466
0.4547 7.0796 1600 3.2960
0.4377 7.3009 1650 3.6329
0.3454 7.5221 1700 3.7038
0.6575 7.7434 1750 3.6313
0.3357 7.9646 1800 3.9394
0.2812 8.1858 1850 3.7570
0.278 8.4071 1900 3.9145
0.3365 8.6283 1950 3.7289
0.4358 8.8496 2000 3.3832
0.2653 9.0708 2050 3.6875
0.2302 9.2920 2100 3.8430
0.1409 9.5133 2150 4.0128
0.3695 9.7345 2200 3.5634
0.2317 9.9558 2250 4.5010
0.3039 10.1770 2300 4.3949
0.2396 10.3982 2350 4.1234
0.2696 10.6195 2400 3.9876
0.2627 10.8407 2450 4.0118
0.2415 11.0619 2500 4.0133
0.062 11.2832 2550 4.1836
0.2313 11.5044 2600 4.2826
0.1002 11.7257 2650 4.4694
0.0836 11.9469 2700 4.6534
0.1351 12.1681 2750 4.3303
0.0415 12.3894 2800 4.4617
0.1199 12.6106 2850 4.5453
0.106 12.8319 2900 4.5849
0.1003 13.0531 2950 4.7043
0.0116 13.2743 3000 4.8034
0.0372 13.4956 3050 4.8729
0.0587 13.7168 3100 4.7357
0.1131 13.9381 3150 4.2960
0.0582 14.1593 3200 4.2865
0.0746 14.3805 3250 4.5552
0.1061 14.6018 3300 4.5042
0.108 14.8230 3350 4.5374
0.0118 15.0442 3400 4.7829
0.0579 15.2655 3450 4.8695
0.0358 15.4867 3500 4.9450
0.0772 15.7080 3550 4.9850
0.0838 15.9292 3600 4.9220
0.0478 16.1504 3650 4.8603
0.1258 16.3717 3700 5.0143
0.0192 16.5929 3750 5.0035
0.0856 16.8142 3800 5.0450
0.0079 17.0354 3850 5.0792
0.0075 17.2566 3900 5.0261
0.0647 17.4779 3950 5.0301
0.0296 17.6991 4000 4.9634
0.0044 17.9204 4050 4.9916
0.0117 18.1416 4100 4.9851
0.0047 18.3628 4150 4.9993
0.0034 18.5841 4200 5.0673
0.0466 18.8053 4250 5.0642
0.0362 19.0265 4300 5.0544
0.0145 19.2478 4350 5.0634
0.0125 19.4690 4400 5.0688
0.0063 19.6903 4450 5.0728
0.0231 19.9115 4500 5.0749

Framework versions

  • Transformers 4.43.3
  • Pytorch 2.4.0+cpu
  • Datasets 2.20.0
  • Tokenizers 0.19.1