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git-base-pokemon

This model is a fine-tuned version of microsoft/git-base on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0396
  • Wer Score: 6.0488

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Score
7.3484 1.06 50 4.4320 10.6547
2.1536 2.13 100 0.2910 1.8947
0.0909 3.19 150 0.0322 0.3684
0.0278 4.26 200 0.0275 0.3659
0.0211 5.32 250 0.0271 0.8858
0.0185 6.38 300 0.0267 0.6778
0.0155 7.45 350 0.0272 7.8190
0.0129 8.51 400 0.0279 3.2452
0.0108 9.57 450 0.0280 15.0462
0.0082 10.64 500 0.0291 10.0372
0.0069 11.7 550 0.0303 15.1592
0.0048 12.77 600 0.0321 15.4493
0.0033 13.83 650 0.0322 16.2439
0.0022 14.89 700 0.0350 17.7125
0.0017 15.96 750 0.0340 16.8357
0.0011 17.02 800 0.0354 16.8780
0.0009 18.09 850 0.0351 17.3273
0.0006 19.15 900 0.0364 16.4788
0.0005 20.21 950 0.0368 15.4442
0.0004 21.28 1000 0.0368 16.2336
0.0004 22.34 1050 0.0375 14.1168
0.0004 23.4 1100 0.0375 14.4365
0.0004 24.47 1150 0.0373 12.3890
0.0004 25.53 1200 0.0379 8.7843
0.0004 26.6 1250 0.0382 9.2298
0.0003 27.66 1300 0.0383 8.8562
0.0003 28.72 1350 0.0384 9.5777
0.0003 29.79 1400 0.0383 8.6021
0.0003 30.85 1450 0.0387 7.9782
0.0003 31.91 1500 0.0387 7.7394
0.0003 32.98 1550 0.0388 7.6431
0.0003 34.04 1600 0.0389 6.9037
0.0003 35.11 1650 0.0391 6.8665
0.0003 36.17 1700 0.0392 6.0526
0.0003 37.23 1750 0.0394 5.6996
0.0003 38.3 1800 0.0393 6.1361
0.0003 39.36 1850 0.0394 5.9127
0.0003 40.43 1900 0.0394 5.6816
0.0003 41.49 1950 0.0394 5.3723
0.0003 42.55 2000 0.0395 4.8806
0.0002 43.62 2050 0.0395 6.9178
0.0002 44.68 2100 0.0395 6.2953
0.0002 45.74 2150 0.0395 6.1142
0.0002 46.81 2200 0.0396 6.0642
0.0002 47.87 2250 0.0396 6.0077
0.0002 48.94 2300 0.0396 6.0026
0.0002 50.0 2350 0.0396 6.0488

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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