layoutlmv2-base-uncased_finetuned_docvqa

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: 4.7623

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
5.2477 0.2212 50 4.4955
4.4322 0.4425 100 4.0859
4.2098 0.6637 150 3.8177
3.8664 0.8850 200 3.5806
3.5251 1.1062 250 3.5032
3.1732 1.3274 300 3.2472
3.0841 1.5487 350 3.0947
2.8536 1.7699 400 2.7800
2.4276 1.9912 450 2.7769
2.06 2.2124 500 2.6578
1.8173 2.4336 550 2.6715
2.1107 2.6549 600 2.5620
2.1352 2.8761 650 2.3209
1.5368 3.0973 700 2.2305
1.3107 3.3186 750 2.6482
1.4519 3.5398 800 2.3794
1.2756 3.7611 850 2.3672
1.2282 3.9823 900 2.2342
1.0882 4.2035 950 2.7453
0.9957 4.4248 1000 2.7899
1.0055 4.6460 1050 2.7979
0.9377 4.8673 1100 2.5045
1.0285 5.0885 1150 2.4579
0.6299 5.3097 1200 2.7204
0.8789 5.5310 1250 2.6098
0.5642 5.7522 1300 2.7831
0.6949 5.9735 1350 3.0893
0.4063 6.1947 1400 2.8284
0.5117 6.4159 1450 3.0634
0.4416 6.6372 1500 3.3999
0.4999 6.8584 1550 3.2898
0.5086 7.0796 1600 3.4221
0.3996 7.3009 1650 3.0418
0.235 7.5221 1700 3.3613
0.4907 7.7434 1750 3.1062
0.3033 7.9646 1800 4.0306
0.3901 8.1858 1850 3.8258
0.3625 8.4071 1900 3.2560
0.3074 8.6283 1950 3.6874
0.3582 8.8496 2000 3.2337
0.2091 9.0708 2050 3.2660
0.2416 9.2920 2100 3.4408
0.1241 9.5133 2150 3.6883
0.2945 9.7345 2200 3.5552
0.2575 9.9558 2250 3.3925
0.258 10.1770 2300 3.8662
0.1662 10.3982 2350 3.6742
0.1491 10.6195 2400 4.3579
0.2379 10.8407 2450 4.1496
0.0899 11.0619 2500 4.2631
0.026 11.2832 2550 4.3676
0.1356 11.5044 2600 4.1160
0.0734 11.7257 2650 3.8254
0.2507 11.9469 2700 3.9717
0.1241 12.1681 2750 3.7671
0.0207 12.3894 2800 3.9668
0.0662 12.6106 2850 4.0811
0.1262 12.8319 2900 3.9894
0.0483 13.0531 2950 4.0627
0.0889 13.2743 3000 4.1365
0.0311 13.4956 3050 4.1390
0.0992 13.7168 3100 4.0020
0.1021 13.9381 3150 3.8962
0.109 14.1593 3200 4.2122
0.0164 14.3805 3250 4.3584
0.0663 14.6018 3300 4.1452
0.0702 14.8230 3350 4.2793
0.0435 15.0442 3400 4.3782
0.0504 15.2655 3450 4.3851
0.0185 15.4867 3500 4.6016
0.0795 15.7080 3550 4.5381
0.049 15.9292 3600 4.2093
0.0608 16.1504 3650 4.3391
0.0953 16.3717 3700 4.2657
0.0603 16.5929 3750 4.4624
0.0312 16.8142 3800 4.3063
0.0038 17.0354 3850 4.4603
0.0271 17.2566 3900 4.4354
0.0094 17.4779 3950 4.6563
0.019 17.6991 4000 4.7925
0.045 17.9204 4050 4.6123
0.0112 18.1416 4100 4.6376
0.0348 18.3628 4150 4.6756
0.0216 18.5841 4200 4.7026
0.009 18.8053 4250 4.7217
0.0356 19.0265 4300 4.7260
0.0479 19.2478 4350 4.7143
0.0114 19.4690 4400 4.7547
0.0069 19.6903 4450 4.7605
0.0053 19.9115 4500 4.7623

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.5.0+cu121
  • Datasets 3.0.2
  • Tokenizers 0.19.1
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