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test-model

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metadata
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: test-model
    results: []

test-model

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: 5.7326

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss
0.5222 0.2212 50 3.3167
0.3741 0.4425 100 3.5175
0.5087 0.6637 150 3.8062
0.7125 0.8850 200 4.1227
0.4148 1.1062 250 4.1049
0.3261 1.3274 300 3.8527
0.3721 1.5487 350 4.1329
0.3444 1.7699 400 4.0143
0.7134 1.9912 450 3.9609
0.2702 2.2124 500 4.3720
0.4396 2.4336 550 4.0561
0.4176 2.6549 600 3.8587
0.3063 2.8761 650 3.7568
0.2262 3.0973 700 4.1540
0.2206 3.3186 750 4.4668
0.3822 3.5398 800 4.1496
0.2665 3.7611 850 4.0747
0.3072 3.9823 900 4.8918
0.2284 4.2035 950 4.1878
0.1628 4.4248 1000 4.7544
0.1525 4.6460 1050 4.5190
0.2555 4.8673 1100 4.3845
0.3933 5.0885 1150 4.0418
0.0484 5.3097 1200 4.6753
0.3433 5.5310 1250 4.1875
0.2453 5.7522 1300 4.1922
0.0677 5.9735 1350 4.7340
0.1835 6.1947 1400 4.6262
0.2394 6.4159 1450 4.4393
0.1599 6.6372 1500 5.3708
0.2705 6.8584 1550 3.9829
0.2067 7.0796 1600 4.3178
0.2859 7.3009 1650 3.7816
0.1232 7.5221 1700 4.1798
0.1811 7.7434 1750 4.3196
0.1274 7.9646 1800 4.6113
0.1361 8.1858 1850 4.2228
0.1533 8.4071 1900 3.8230
0.1179 8.6283 1950 4.0259
0.1265 8.8496 2000 4.8971
0.2258 9.0708 2050 4.7136
0.0998 9.2920 2100 4.8828
0.1154 9.5133 2150 4.2313
0.1426 9.7345 2200 3.8275
0.0924 9.9558 2250 4.2277
0.0889 10.1770 2300 4.0426
0.0495 10.3982 2350 4.2849
0.0538 10.6195 2400 4.6157
0.0318 10.8407 2450 5.0160
0.0957 11.0619 2500 5.1147
0.0417 11.2832 2550 4.8833
0.0244 11.5044 2600 5.1067
0.0799 11.7257 2650 4.8877
0.0365 11.9469 2700 5.0193
0.0901 12.1681 2750 4.7789
0.0088 12.3894 2800 4.7324
0.0498 12.6106 2850 4.6903
0.0809 12.8319 2900 4.4701
0.035 13.0531 2950 4.6233
0.0486 13.2743 3000 4.8579
0.0231 13.4956 3050 4.9399
0.0677 13.7168 3100 4.8829
0.028 13.9381 3150 5.1337
0.0243 14.1593 3200 5.3634
0.0248 14.3805 3250 5.4940
0.0422 14.6018 3300 5.6946
0.0654 14.8230 3350 5.3121
0.0134 15.0442 3400 5.3630
0.0405 15.2655 3450 5.3444
0.0213 15.4867 3500 5.3298
0.0125 15.7080 3550 5.3672
0.0623 15.9292 3600 5.3024
0.0032 16.1504 3650 5.2147
0.0149 16.3717 3700 5.3809
0.0248 16.5929 3750 5.4913
0.03 16.8142 3800 5.3860
0.0007 17.0354 3850 5.3717
0.0032 17.2566 3900 5.5140
0.0029 17.4779 3950 5.4480
0.0055 17.6991 4000 5.4522
0.0349 17.9204 4050 5.5724
0.0007 18.1416 4100 5.6047
0.0006 18.3628 4150 5.6135
0.0007 18.5841 4200 5.6239
0.0077 18.8053 4250 5.6692
0.009 19.0265 4300 5.6640
0.009 19.2478 4350 5.6822
0.0007 19.4690 4400 5.7342
0.0073 19.6903 4450 5.7320
0.015 19.9115 4500 5.7326

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.5.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.20.3