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: 4.3701

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
5.3439 0.22 50 4.6661
4.4287 0.44 100 4.2125
4.0781 0.66 150 3.7145
3.8419 0.88 200 3.5926
3.633 1.11 250 3.4266
3.1492 1.33 300 3.2833
3.2018 1.55 350 3.0237
2.8891 1.77 400 2.9976
2.6695 1.99 450 3.0654
2.211 2.21 500 2.7658
2.2555 2.43 550 2.5009
2.0293 2.65 600 2.3515
1.8663 2.88 650 2.1264
1.6332 3.1 700 2.3934
1.5298 3.32 750 2.6686
1.3594 3.54 800 2.0781
1.3923 3.76 850 2.2174
1.26 3.98 900 2.5728
0.9371 4.2 950 2.7164
0.9891 4.42 1000 2.8637
1.0822 4.65 1050 2.5435
0.9355 4.87 1100 2.7256
0.9527 5.09 1150 2.8934
0.9454 5.31 1200 3.0235
0.6458 5.53 1250 3.2280
0.8697 5.75 1300 2.8636
0.9365 5.97 1350 2.8955
0.4425 6.19 1400 3.0859
0.6329 6.42 1450 3.0695
0.7564 6.64 1500 2.5050
0.5747 6.86 1550 3.2825
0.5451 7.08 1600 3.4123
0.5432 7.3 1650 3.1163
0.3738 7.52 1700 2.8969
0.5026 7.74 1750 2.8579
0.4245 7.96 1800 3.2212
0.3145 8.19 1850 3.4482
0.516 8.41 1900 2.9995
0.2816 8.63 1950 2.9903
0.3946 8.85 2000 3.3378
0.3854 9.07 2050 3.4644
0.2191 9.29 2100 3.5034
0.3854 9.51 2150 3.4320
0.2207 9.73 2200 3.6972
0.2779 9.96 2250 3.6866
0.2837 10.18 2300 3.8988
0.1613 10.4 2350 3.8722
0.1069 10.62 2400 3.9079
0.4031 10.84 2450 3.5352
0.2129 11.06 2500 3.6764
0.1166 11.28 2550 4.1964
0.1599 11.5 2600 4.2577
0.2108 11.73 2650 3.7519
0.3207 11.95 2700 3.6609
0.2291 12.17 2750 3.5265
0.1609 12.39 2800 3.8727
0.2308 12.61 2850 3.9877
0.1461 12.83 2900 4.0395
0.1353 13.05 2950 3.8678
0.0735 13.27 3000 3.9054
0.1695 13.5 3050 3.5661
0.0129 13.72 3100 3.9625
0.1691 13.94 3150 3.7996
0.066 14.16 3200 4.2540
0.0484 14.38 3250 4.0625
0.099 14.6 3300 4.4666
0.127 14.82 3350 4.0762
0.046 15.04 3400 4.1859
0.0608 15.27 3450 4.4004
0.1002 15.49 3500 4.3228
0.0394 15.71 3550 4.4576
0.0863 15.93 3600 4.4386
0.0214 16.15 3650 4.5233
0.0661 16.37 3700 4.4493
0.0448 16.59 3750 4.3361
0.0463 16.81 3800 4.4174
0.0456 17.04 3850 4.4851
0.0144 17.26 3900 4.4655
0.0268 17.48 3950 4.4417
0.0529 17.7 4000 4.3580
0.0678 17.92 4050 4.2008
0.0039 18.14 4100 4.2346
0.0481 18.36 4150 4.2652
0.0501 18.58 4200 4.2786
0.0271 18.81 4250 4.2857
0.0322 19.03 4300 4.3047
0.0187 19.25 4350 4.3691
0.0469 19.47 4400 4.3560
0.0056 19.69 4450 4.3626
0.0099 19.91 4500 4.3701

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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