layoutlmv2-base-uncased_finetuned_docvqa

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 4.8430

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
5.3379 0.22 50 4.6257
4.4305 0.44 100 4.2230
4.0588 0.66 150 3.9539
3.7822 0.88 200 3.7040
3.4957 1.11 250 3.4754
3.2417 1.33 300 3.1954
2.8607 1.55 350 2.8809
2.6602 1.77 400 2.9741
2.621 1.99 450 2.8658
2.1733 2.21 500 2.7248
2.106 2.43 550 2.4072
1.8389 2.65 600 2.4147
1.7862 2.88 650 2.2116
1.4224 3.1 700 2.4379
1.4773 3.32 750 2.4346
1.2225 3.54 800 2.5779
1.5368 3.76 850 2.4343
1.479 3.98 900 2.1432
0.7982 4.2 950 2.5897
0.8336 4.42 1000 2.8477
1.0647 4.65 1050 2.7111
0.8795 4.87 1100 2.5601
0.9265 5.09 1150 2.9547
0.7111 5.31 1200 3.1621
0.7244 5.53 1250 2.7862
0.9501 5.75 1300 2.4007
0.7424 5.97 1350 2.9918
0.4422 6.19 1400 3.5247
0.5952 6.42 1450 2.8743
0.7173 6.64 1500 2.7440
0.6311 6.86 1550 2.9658
0.393 7.08 1600 3.0994
0.3655 7.3 1650 3.3074
0.3432 7.52 1700 3.1921
0.5986 7.74 1750 3.3517
0.5456 7.96 1800 3.1552
0.565 8.19 1850 2.9922
0.3902 8.41 1900 3.6814
0.3408 8.63 1950 3.2820
0.241 8.85 2000 3.5644
0.3172 9.07 2050 3.4752
0.294 9.29 2100 3.7023
0.2993 9.51 2150 3.5031
0.0928 9.73 2200 4.0305
0.4598 9.96 2250 3.4260
0.2795 10.18 2300 3.2730
0.0887 10.4 2350 3.7174
0.3682 10.62 2400 3.4060
0.1924 10.84 2450 4.1368
0.1825 11.06 2500 4.1640
0.1987 11.28 2550 3.9908
0.0875 11.5 2600 4.1872
0.1719 11.73 2650 3.9948
0.2844 11.95 2700 4.1731
0.1085 12.17 2750 3.9568
0.1496 12.39 2800 3.9272
0.0701 12.61 2850 4.2957
0.1617 12.83 2900 4.2806
0.0934 13.05 2950 4.3200
0.0405 13.27 3000 4.1869
0.0898 13.5 3050 4.1207
0.189 13.72 3100 4.4437
0.0798 13.94 3150 4.6480
0.1199 14.16 3200 4.4105
0.0922 14.38 3250 4.4321
0.1556 14.6 3300 4.3353
0.1933 14.82 3350 4.0635
0.0164 15.04 3400 4.1792
0.064 15.27 3450 4.2202
0.0914 15.49 3500 4.2382
0.0287 15.71 3550 4.4255
0.1054 15.93 3600 4.5788
0.0306 16.15 3650 4.7566
0.0297 16.37 3700 4.6610
0.0529 16.59 3750 4.6494
0.0729 16.81 3800 4.6314
0.0388 17.04 3850 4.6675
0.0207 17.26 3900 4.7816
0.0889 17.48 3950 4.6941
0.0058 17.7 4000 4.6818
0.0068 17.92 4050 4.7755
0.0222 18.14 4100 4.7658
0.1152 18.36 4150 4.8247
0.0181 18.58 4200 4.8290
0.0349 18.81 4250 4.7989
0.0165 19.03 4300 4.8208
0.029 19.25 4350 4.8401
0.0073 19.47 4400 4.8544
0.0277 19.69 4450 4.8356
0.0164 19.91 4500 4.8430

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0+cu117
  • Datasets 2.11.0
  • Tokenizers 0.13.2
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