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

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: 4.5637

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
5.2825 0.2212 50 4.5028
4.4224 0.4425 100 4.0697
4.1296 0.6637 150 3.9535
3.8325 0.8850 200 3.5453
3.4315 1.1062 250 3.8942
3.2797 1.3274 300 3.2322
3.0041 1.5487 350 2.9298
2.7424 1.7699 400 2.6607
2.3969 1.9912 450 2.6309
2.0661 2.2124 500 2.7708
1.9442 2.4336 550 2.6028
1.8616 2.6549 600 2.2959
1.9612 2.8761 650 2.1755
1.5795 3.0973 700 2.3348
1.3558 3.3186 750 2.3090
1.4052 3.5398 800 2.5506
1.4666 3.7611 850 2.4862
1.3408 3.9823 900 2.4843
1.1343 4.2035 950 2.5429
1.0681 4.4248 1000 2.6741
1.2315 4.6460 1050 3.5725
1.3005 4.8673 1100 2.5506
0.9839 5.0885 1150 2.5715
0.8055 5.3097 1200 3.1745
1.0878 5.5310 1250 3.6689
0.7227 5.7522 1300 3.4183
0.7872 5.9735 1350 2.9087
0.5348 6.1947 1400 2.9912
0.8189 6.4159 1450 2.7346
0.5305 6.6372 1500 2.8126
0.5597 6.8584 1550 3.2278
0.7229 7.0796 1600 2.8248
0.4531 7.3009 1650 3.2564
0.5035 7.5221 1700 3.0984
0.4971 7.7434 1750 3.0667
0.5544 7.9646 1800 2.8519
0.2919 8.1858 1850 3.9333
0.405 8.4071 1900 3.5871
0.3391 8.6283 1950 3.5935
0.5292 8.8496 2000 3.4899
0.471 9.0708 2050 3.5509
0.2564 9.2920 2100 3.9214
0.4863 9.5133 2150 3.6614
0.315 9.7345 2200 3.9714
0.4728 9.9558 2250 3.5600
0.2982 10.1770 2300 3.6403
0.1714 10.3982 2350 3.6444
0.205 10.6195 2400 4.3221
0.3756 10.8407 2450 4.0914
0.1874 11.0619 2500 3.8238
0.0965 11.2832 2550 4.0389
0.2563 11.5044 2600 4.1470
0.2176 11.7257 2650 3.8725
0.147 11.9469 2700 4.0304
0.2113 12.1681 2750 3.7899
0.1281 12.3894 2800 4.1518
0.1377 12.6106 2850 4.1490
0.2311 12.8319 2900 4.0664
0.2286 13.0531 2950 3.8024
0.1179 13.2743 3000 3.9863
0.1248 13.4956 3050 3.9647
0.1693 13.7168 3100 3.6965
0.0876 13.9381 3150 4.1747
0.12 14.1593 3200 4.2850
0.0899 14.3805 3250 4.2602
0.1521 14.6018 3300 4.0035
0.1186 14.8230 3350 4.0200
0.0194 15.0442 3400 4.2270
0.0629 15.2655 3450 4.3293
0.0155 15.4867 3500 4.2434
0.1116 15.7080 3550 4.2031
0.0691 15.9292 3600 4.4198
0.0235 16.1504 3650 4.4143
0.0857 16.3717 3700 4.2814
0.0114 16.5929 3750 4.2801
0.0176 16.8142 3800 4.4927
0.0978 17.0354 3850 4.5054
0.0122 17.2566 3900 4.5038
0.009 17.4779 3950 4.5913
0.0476 17.6991 4000 4.6732
0.0985 17.9204 4050 4.5039
0.0051 18.1416 4100 4.5284
0.0696 18.3628 4150 4.5731
0.0238 18.5841 4200 4.5257
0.0068 18.8053 4250 4.5459
0.0276 19.0265 4300 4.5551
0.0296 19.2478 4350 4.5593
0.0068 19.4690 4400 4.5723
0.1252 19.6903 4450 4.5622
0.0051 19.9115 4500 4.5637

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.5.0+cu121
  • Datasets 3.1.0
  • Tokenizers 0.19.1