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layoutlmv2-base-uncased_finetuned_docvqa
3fb71c0
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: 4.9097

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
5.3187 0.22 50 4.6843
4.5031 0.44 100 4.1274
4.128 0.66 150 3.7921
3.9091 0.88 200 3.6115
3.6889 1.11 250 3.5696
3.1707 1.33 300 3.1811
3.0354 1.55 350 2.8562
2.7856 1.77 400 2.7444
2.4529 1.99 450 2.7386
1.9655 2.21 500 2.6058
1.9712 2.43 550 2.3241
1.9305 2.65 600 2.2414
1.7701 2.88 650 2.1765
1.3844 3.1 700 2.7470
1.4493 3.32 750 2.3821
1.2487 3.54 800 2.0868
1.3189 3.76 850 2.5860
1.3709 3.98 900 2.4280
1.1087 4.2 950 2.5985
0.9481 4.42 1000 3.3342
1.0106 4.65 1050 3.0318
0.9703 4.87 1100 2.7671
0.788 5.09 1150 3.0565
0.6952 5.31 1200 3.3631
0.8017 5.53 1250 3.2544
0.6489 5.75 1300 3.0772
0.7419 5.97 1350 2.6226
0.558 6.19 1400 3.5186
0.5727 6.42 1450 3.3600
0.5322 6.64 1500 3.5181
0.6472 6.86 1550 3.6967
0.5805 7.08 1600 3.2425
0.4877 7.3 1650 3.4871
0.273 7.52 1700 3.5272
0.5527 7.74 1750 3.0758
0.2936 7.96 1800 3.3492
0.2585 8.19 1850 3.4836
0.5038 8.41 1900 3.3159
0.3943 8.63 1950 3.3977
0.3129 8.85 2000 3.8042
0.2977 9.07 2050 3.7062
0.2695 9.29 2100 3.7420
0.363 9.51 2150 3.6655
0.1834 9.73 2200 3.7858
0.3347 9.96 2250 3.9257
0.2159 10.18 2300 3.9075
0.2691 10.4 2350 3.8001
0.2965 10.62 2400 3.6177
0.3078 10.84 2450 3.8440
0.1397 11.06 2500 4.0490
0.052 11.28 2550 4.2137
0.1741 11.5 2600 4.2273
0.2956 11.73 2650 3.8075
0.2098 11.95 2700 4.1653
0.1466 12.17 2750 4.2080
0.1378 12.39 2800 4.0473
0.1864 12.61 2850 4.0665
0.1938 12.83 2900 4.1019
0.2332 13.05 2950 3.9249
0.0486 13.27 3000 4.2374
0.223 13.5 3050 4.1405
0.1387 13.72 3100 4.1833
0.1447 13.94 3150 4.0949
0.0265 14.16 3200 4.2652
0.0253 14.38 3250 4.5294
0.1781 14.6 3300 4.5310
0.0676 14.82 3350 4.3897
0.0692 15.04 3400 4.3176
0.1093 15.27 3450 4.3505
0.0219 15.49 3500 4.4436
0.0834 15.71 3550 4.4736
0.0728 15.93 3600 4.5234
0.0612 16.15 3650 4.6432
0.0844 16.37 3700 4.5729
0.0269 16.59 3750 4.7230
0.037 16.81 3800 4.7512
0.0532 17.04 3850 4.8247
0.0042 17.26 3900 4.8263
0.0684 17.48 3950 4.7495
0.0058 17.7 4000 4.7748
0.0158 17.92 4050 4.8827
0.0059 18.14 4100 4.8746
0.0217 18.36 4150 4.8981
0.0725 18.58 4200 4.9908
0.0399 18.81 4250 4.8712
0.0478 19.03 4300 4.9006
0.0212 19.25 4350 4.9160
0.0269 19.47 4400 4.9076
0.0317 19.69 4450 4.9081
0.0233 19.91 4500 4.9097

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1