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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: 4.4147

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
5.2445 0.2212 50 4.5117
4.4073 0.4425 100 4.2219
4.1282 0.6637 150 3.8024
3.8337 0.8850 200 3.6136
3.5425 1.1062 250 3.4874
3.3022 1.3274 300 3.1532
3.2627 1.5487 350 3.0497
2.9534 1.7699 400 3.0351
2.7062 1.9912 450 3.0241
2.3171 2.2124 500 2.6357
2.0499 2.4336 550 2.3957
1.9222 2.6549 600 2.4198
1.8248 2.8761 650 2.5430
1.5168 3.0973 700 4.2506
1.4417 3.3186 750 2.3309
1.3993 3.5398 800 2.3422
1.3945 3.7611 850 2.0276
1.138 3.9823 900 2.3541
1.2168 4.2035 950 2.8071
1.1358 4.4248 1000 2.6772
1.0205 4.6460 1050 2.7978
0.9784 4.8673 1100 3.3150
1.001 5.0885 1150 2.5239
0.8487 5.3097 1200 2.9815
1.0126 5.5310 1250 3.2436
0.6363 5.7522 1300 3.2784
0.9224 5.9735 1350 3.4480
0.6946 6.1947 1400 3.1487
0.6052 6.4159 1450 3.4397
0.5203 6.6372 1500 2.9999
0.6589 6.8584 1550 3.2889
0.6399 7.0796 1600 3.1920
0.4313 7.3009 1650 2.9790
0.3867 7.5221 1700 3.4399
0.5132 7.7434 1750 3.0626
0.4955 7.9646 1800 3.2692
0.3658 8.1858 1850 3.4662
0.2021 8.4071 1900 3.7119
0.394 8.6283 1950 3.5633
0.4442 8.8496 2000 3.7246
0.3807 9.0708 2050 3.5174
0.2692 9.2920 2100 3.8268
0.3595 9.5133 2150 3.6366
0.3495 9.7345 2200 3.5126
0.3814 9.9558 2250 3.4845
0.2319 10.1770 2300 3.5154
0.1587 10.3982 2350 3.9049
0.2771 10.6195 2400 3.9095
0.2156 10.8407 2450 3.9481
0.1906 11.0619 2500 3.9076
0.2064 11.2832 2550 3.9890
0.1756 11.5044 2600 3.8731
0.1934 11.7257 2650 3.8914
0.1177 11.9469 2700 4.0169
0.2135 12.1681 2750 3.6795
0.1198 12.3894 2800 3.9709
0.1219 12.6106 2850 3.7425
0.1073 12.8319 2900 4.2397
0.1428 13.0531 2950 3.9107
0.0728 13.2743 3000 4.2249
0.0516 13.4956 3050 3.9716
0.1044 13.7168 3100 4.2036
0.2026 13.9381 3150 3.8552
0.1182 14.1593 3200 4.0365
0.0368 14.3805 3250 4.3629
0.0331 14.6018 3300 4.4697
0.1629 14.8230 3350 3.9966
0.0619 15.0442 3400 4.1223
0.0167 15.2655 3450 4.2150
0.0602 15.4867 3500 4.1427
0.1045 15.7080 3550 3.9883
0.0629 15.9292 3600 4.1485
0.0492 16.1504 3650 3.9531
0.0657 16.3717 3700 4.2826
0.0354 16.5929 3750 4.1867
0.0327 16.8142 3800 4.1699
0.0045 17.0354 3850 4.1904
0.0139 17.2566 3900 4.2937
0.0373 17.4779 3950 4.1179
0.039 17.6991 4000 4.1837
0.0717 17.9204 4050 4.2483
0.0316 18.1416 4100 4.2423
0.0041 18.3628 4150 4.2651
0.0268 18.5841 4200 4.3379
0.0156 18.8053 4250 4.3978
0.0265 19.0265 4300 4.3942
0.0461 19.2478 4350 4.4056
0.0195 19.4690 4400 4.4082
0.0105 19.6903 4450 4.4160
0.0387 19.9115 4500 4.4147

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1