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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: 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: 5.2363

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
4.0408 0.2212 50 4.0001
4.1144 0.4425 100 3.7920
3.8854 0.6637 150 3.6503
3.6048 0.8850 200 3.3228
3.1846 1.1062 250 3.6110
2.917 1.3274 300 2.9913
2.8848 1.5487 350 2.7110
2.5842 1.7699 400 2.4111
2.1162 1.9912 450 2.4839
1.8347 2.2124 500 2.7160
1.786 2.4336 550 2.5238
1.8828 2.6549 600 2.4274
1.8181 2.8761 650 2.5544
1.5656 3.0973 700 2.4362
1.4265 3.3186 750 2.9550
1.4967 3.5398 800 3.2754
1.2732 3.7611 850 3.0296
1.3162 3.9823 900 2.6941
1.0837 4.2035 950 2.9119
1.1094 4.4248 1000 3.0181
1.1846 4.6460 1050 2.6419
1.5768 4.8673 1100 4.0184
1.4084 5.0885 1150 3.1371
0.9783 5.3097 1200 2.9210
0.984 5.5310 1250 3.0042
0.7546 5.7522 1300 3.1277
0.799 5.9735 1350 3.0501
0.6629 6.1947 1400 3.2626
0.8973 6.4159 1450 3.2922
0.6816 6.6372 1500 3.0462
0.539 6.8584 1550 3.1018
0.6871 7.0796 1600 3.1925
0.4569 7.3009 1650 3.2120
0.6451 7.5221 1700 2.9812
0.5579 7.7434 1750 3.3052
0.4851 7.9646 1800 4.1491
0.5851 8.1858 1850 3.5338
0.4344 8.4071 1900 3.4542
0.5021 8.6283 1950 3.2402
0.4699 8.8496 2000 3.3066
0.4668 9.0708 2050 3.6041
0.2258 9.2920 2100 3.6862
0.4708 9.5133 2150 3.7622
0.3933 9.7345 2200 3.7370
0.3858 9.9558 2250 3.3631
0.3359 10.1770 2300 3.6203
0.2365 10.3982 2350 3.7388
0.3147 10.6195 2400 3.8653
0.3401 10.8407 2450 4.0243
0.1644 11.0619 2500 4.1857
0.142 11.2832 2550 4.3611
0.266 11.5044 2600 4.2761
0.1592 11.7257 2650 4.3012
0.1126 11.9469 2700 4.3518
0.1409 12.1681 2750 4.4466
0.0731 12.3894 2800 4.3459
0.1243 12.6106 2850 4.3446
0.2672 12.8319 2900 4.3548
0.228 13.0531 2950 4.1020
0.0622 13.2743 3000 4.4363
0.1287 13.4956 3050 4.5345
0.1974 13.7168 3100 4.6727
0.2213 13.9381 3150 4.3807
0.1551 14.1593 3200 4.4805
0.1295 14.3805 3250 4.7027
0.0664 14.6018 3300 4.7583
0.1159 14.8230 3350 4.3252
0.02 15.0442 3400 4.6594
0.0438 15.2655 3450 4.8679
0.0495 15.4867 3500 5.1235
0.1143 15.7080 3550 5.1614
0.1405 15.9292 3600 5.1302
0.0351 16.1504 3650 5.0780
0.1258 16.3717 3700 5.1000
0.0387 16.5929 3750 5.0849
0.0809 16.8142 3800 4.9809
0.0955 17.0354 3850 5.0030
0.0347 17.2566 3900 5.0040
0.0716 17.4779 3950 4.9608
0.0417 17.6991 4000 5.0922
0.1394 17.9204 4050 5.1081
0.0612 18.1416 4100 5.1859
0.0057 18.3628 4150 5.2126
0.0965 18.5841 4200 5.1589
0.0131 18.8053 4250 5.1224
0.0922 19.0265 4300 5.1521
0.0353 19.2478 4350 5.1961
0.0351 19.4690 4400 5.2249
0.0161 19.6903 4450 5.2304
0.0095 19.9115 4500 5.2363

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1