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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: 4.4423

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
5.2693 0.22 50 4.4222
4.3703 0.44 100 4.1079
4.1363 0.66 150 3.9209
3.7332 0.88 200 3.6332
3.4591 1.11 250 3.5577
3.1781 1.33 300 3.1319
3.3388 1.55 350 3.0866
2.8356 1.77 400 2.7820
2.4286 1.99 450 2.8378
2.0496 2.21 500 2.5224
1.9469 2.43 550 2.5281
1.8342 2.65 600 2.5674
1.6589 2.88 650 2.2914
1.6939 3.1 700 2.4427
1.3883 3.32 750 2.5626
1.3944 3.54 800 2.3736
1.2459 3.76 850 2.7994
1.5218 3.98 900 2.5390
1.1471 4.2 950 2.5951
0.8888 4.42 1000 2.7430
0.971 4.65 1050 2.5219
1.0425 4.87 1100 2.5474
0.7665 5.09 1150 2.9321
0.8039 5.31 1200 2.7369
0.6426 5.53 1250 3.1309
0.6628 5.75 1300 3.1167
0.906 5.97 1350 3.8550
0.6223 6.19 1400 3.4892
0.6274 6.42 1450 3.2927
0.4732 6.64 1500 3.4192
0.5962 6.86 1550 3.2867
0.6761 7.08 1600 3.0610
0.4096 7.3 1650 3.5926
0.457 7.52 1700 3.2824
0.3721 7.74 1750 3.4383
0.4547 7.96 1800 3.4794
0.4231 8.19 1850 3.7591
0.3292 8.41 1900 3.8104
0.4401 8.63 1950 3.7450
0.446 8.85 2000 3.5815
0.3362 9.07 2050 3.6245
0.1832 9.29 2100 3.7162
0.2085 9.51 2150 3.8565
0.3248 9.73 2200 3.4577
0.4722 9.96 2250 3.6518
0.2575 10.18 2300 3.8701
0.2336 10.4 2350 3.7511
0.2864 10.62 2400 3.7999
0.2091 10.84 2450 3.8716
0.2371 11.06 2500 3.7909
0.1582 11.28 2550 4.0463
0.2519 11.5 2600 3.9798
0.1223 11.73 2650 4.3331
0.1838 11.95 2700 4.1601
0.1204 12.17 2750 4.2846
0.1797 12.39 2800 4.1595
0.123 12.61 2850 4.2625
0.2177 12.83 2900 4.0050
0.1728 13.05 2950 4.0885
0.1525 13.27 3000 3.9733
0.0388 13.5 3050 4.1072
0.0788 13.72 3100 4.2446
0.1629 13.94 3150 4.0483
0.0377 14.16 3200 4.2435
0.0966 14.38 3250 4.1510
0.0943 14.6 3300 4.2591
0.048 14.82 3350 4.1876
0.097 15.04 3400 4.2489
0.0188 15.27 3450 4.3612
0.1163 15.49 3500 4.2931
0.0754 15.71 3550 4.3306
0.1044 15.93 3600 4.2243
0.0316 16.15 3650 4.3932
0.005 16.37 3700 4.4173
0.0389 16.59 3750 4.3939
0.0505 16.81 3800 4.3207
0.0501 17.04 3850 4.3601
0.0491 17.26 3900 4.3211
0.0048 17.48 3950 4.3425
0.0043 17.7 4000 4.3461
0.0309 17.92 4050 4.3733
0.0246 18.14 4100 4.3912
0.0055 18.36 4150 4.4020
0.0078 18.58 4200 4.4256
0.0057 18.81 4250 4.4462
0.0352 19.03 4300 4.4558
0.0451 19.25 4350 4.4557
0.063 19.47 4400 4.4395
0.0123 19.69 4450 4.4428
0.0291 19.91 4500 4.4423

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0