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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: 4.9529

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
5.2903 0.22 50 4.6096
4.441 0.44 100 4.1809
4.1512 0.66 150 3.8270
3.9297 0.88 200 3.6180
3.7006 1.11 250 3.3508
3.1238 1.33 300 3.4886
3.177 1.55 350 3.0878
2.8817 1.77 400 2.8975
2.6113 1.99 450 3.1366
2.9929 2.21 500 4.2811
2.8507 2.43 550 3.1442
2.6294 2.65 600 2.7537
2.9134 2.88 650 4.0845
2.7527 3.1 700 2.6888
2.2184 3.32 750 2.6068
1.9832 3.54 800 2.3920
1.8607 3.76 850 2.3026
1.6756 3.98 900 2.4535
1.594 4.2 950 2.3539
1.3695 4.42 1000 2.9487
1.4473 4.65 1050 2.3269
1.0998 4.87 1100 2.5812
1.043 5.09 1150 2.7785
1.0655 5.31 1200 3.1658
1.2366 5.53 1250 3.5025
1.1033 5.75 1300 3.0308
1.1406 5.97 1350 2.4193
0.7332 6.19 1400 3.0098
0.7752 6.42 1450 3.0226
0.9816 6.64 1500 3.1292
0.794 6.86 1550 3.4569
0.6923 7.08 1600 3.5805
0.4034 7.3 1650 3.9237
0.4836 7.52 1700 3.4433
0.6216 7.74 1750 3.1084
0.6027 7.96 1800 3.5491
0.4783 8.19 1850 3.7448
0.4513 8.41 1900 3.4646
0.4544 8.63 1950 3.7954
0.5161 8.85 2000 3.7831
0.1872 9.07 2050 3.6736
0.506 9.29 2100 3.7390
0.2257 9.51 2150 3.9423
0.2648 9.73 2200 3.7982
0.3953 9.96 2250 3.2984
0.1601 10.18 2300 3.6460
0.2689 10.4 2350 3.9842
0.2762 10.62 2400 3.2707
0.3091 10.84 2450 3.4759
0.2036 11.06 2500 3.7818
0.1104 11.28 2550 3.8338
0.1555 11.5 2600 3.7824
0.2794 11.73 2650 3.7954
0.2728 11.95 2700 3.5966
0.2168 12.17 2750 4.2583
0.1133 12.39 2800 4.3897
0.293 12.61 2850 3.9776
0.1307 12.83 2900 4.4287
0.2012 13.05 2950 4.0434
0.1583 13.27 3000 3.8509
0.1016 13.5 3050 3.9090
0.0329 13.72 3100 4.2917
0.1034 13.94 3150 4.3789
0.0928 14.16 3200 4.4046
0.1318 14.38 3250 4.2611
0.1015 14.6 3300 4.4932
0.1499 14.82 3350 4.4150
0.1858 15.04 3400 4.1948
0.1402 15.27 3450 4.3734
0.0584 15.49 3500 4.3949
0.0288 15.71 3550 4.6144
0.0554 15.93 3600 4.8472
0.0853 16.15 3650 4.7406
0.0111 16.37 3700 5.0774
0.1094 16.59 3750 4.9672
0.0102 16.81 3800 4.9885
0.0884 17.04 3850 5.0612
0.0318 17.26 3900 5.1363
0.1083 17.48 3950 4.7403
0.0891 17.7 4000 4.6907
0.0495 17.92 4050 4.7827
0.015 18.14 4100 5.0118
0.0554 18.36 4150 4.9823
0.084 18.58 4200 4.9539
0.0714 18.81 4250 4.8877
0.0573 19.03 4300 4.9120
0.012 19.25 4350 4.9568
0.0381 19.47 4400 4.9459
0.0126 19.69 4450 4.9544
0.0591 19.91 4500 4.9529

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.13.3