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: 4.5570

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
5.2892 0.22 50 4.6213
4.4958 0.44 100 4.1766
4.1602 0.66 150 3.8565
3.9538 0.88 200 3.5805
3.5903 1.11 250 3.4594
3.1555 1.33 300 3.1305
3.0041 1.55 350 2.9394
2.8133 1.77 400 2.7611
2.5517 1.99 450 2.6359
1.9883 2.21 500 2.6822
2.1057 2.43 550 2.4430
2.0872 2.65 600 2.4388
1.7591 2.88 650 2.3726
1.5244 3.1 700 2.3325
1.5257 3.32 750 2.7668
1.3295 3.54 800 2.2800
1.3167 3.76 850 2.3108
1.2051 3.98 900 2.6429
1.0532 4.2 950 3.1483
1.0167 4.42 1000 2.2237
1.1105 4.65 1050 2.3531
1.006 4.87 1100 2.8026
0.8193 5.09 1150 3.0897
0.7502 5.31 1200 3.5147
0.9308 5.53 1250 2.5877
0.6495 5.75 1300 2.8749
0.7933 5.97 1350 2.7216
0.7199 6.19 1400 3.1257
0.5458 6.42 1450 3.5710
0.7406 6.64 1500 2.5855
0.5537 6.86 1550 2.9661
0.493 7.08 1600 3.3691
0.4938 7.3 1650 3.1937
0.5407 7.52 1700 3.3585
0.5243 7.74 1750 2.7650
0.4177 7.96 1800 3.2618
0.4094 8.19 1850 3.2418
0.4061 8.41 1900 3.2710
0.3248 8.63 1950 3.3732
0.3787 8.85 2000 3.3467
0.2709 9.07 2050 3.1261
0.2655 9.29 2100 3.4552
0.2552 9.51 2150 3.4344
0.3321 9.73 2200 3.5558
0.4165 9.96 2250 3.0775
0.2052 10.18 2300 3.4985
0.2642 10.4 2350 3.8930
0.2461 10.62 2400 3.7029
0.2895 10.84 2450 3.4143
0.1041 11.06 2500 3.6652
0.1316 11.28 2550 3.8867
0.1165 11.5 2600 4.1669
0.1925 11.73 2650 4.3376
0.371 11.95 2700 3.9125
0.1793 12.17 2750 4.0051
0.1771 12.39 2800 3.8161
0.2158 12.61 2850 3.8786
0.1838 12.83 2900 3.7687
0.1364 13.05 2950 4.1103
0.0364 13.27 3000 4.1468
0.2488 13.5 3050 4.1149
0.1046 13.72 3100 4.1887
0.1254 13.94 3150 4.0097
0.033 14.16 3200 4.3023
0.0373 14.38 3250 4.3530
0.0911 14.6 3300 4.2621
0.1014 14.82 3350 3.8815
0.0238 15.04 3400 4.4097
0.1141 15.27 3450 4.4720
0.0278 15.49 3500 4.4407
0.0673 15.71 3550 4.4176
0.0691 15.93 3600 4.4863
0.0379 16.15 3650 4.6924
0.057 16.37 3700 4.5305
0.1087 16.59 3750 4.5050
0.0047 16.81 3800 4.5652
0.051 17.04 3850 4.5482
0.0054 17.26 3900 4.5337
0.0542 17.48 3950 4.5370
0.0132 17.7 4000 4.4744
0.0698 17.92 4050 4.4535
0.0214 18.14 4100 4.4660
0.033 18.36 4150 4.4818
0.029 18.58 4200 4.5033
0.0283 18.81 4250 4.4825
0.013 19.03 4300 4.4794
0.0538 19.25 4350 4.5183
0.0171 19.47 4400 4.5373
0.0122 19.69 4450 4.5501
0.0222 19.91 4500 4.5570

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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