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TrOCR-Sin

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metadata
tags:
  - generated_from_trainer
model-index:
  - name: TrOCR-SIN
    results: []

TrOCR-SIN

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

  • Cer: 0.2343
  • Loss: 0.6859

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 97000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Cer Validation Loss
2.3833 0.36 1000 0.8052 2.3856
2.0754 0.71 2000 0.8113 2.2862
1.9838 1.07 3000 0.8163 2.3398
2.0476 1.42 4000 0.8057 2.1957
2.1071 1.78 5000 0.8085 2.2221
1.8743 2.13 6000 0.8142 2.3726
1.8685 2.49 7000 0.7860 2.2151
1.5893 2.84 8000 0.7558 1.9693
1.3116 3.2 9000 0.7187 1.9843
1.3257 3.55 10000 0.6980 1.9958
1.1866 3.91 11000 0.6662 1.7693
1.0506 4.27 12000 0.6439 1.7593
1.0177 4.62 13000 0.6157 1.6142
0.849 4.98 14000 0.5923 1.5052
0.9062 5.33 15000 0.5733 1.6439
0.9613 5.69 16000 0.5635 1.2713
0.698 6.04 17000 0.5348 1.3989
0.5992 6.4 18000 0.5197 1.5645
0.7429 6.75 19000 0.5132 1.3758
0.5958 7.11 20000 0.4961 1.4102
0.5933 7.47 21000 0.4845 1.2843
0.5802 7.82 22000 0.4760 1.2866
0.5026 8.18 23000 0.4733 1.3028
0.528 8.53 24000 0.4634 1.3796
0.5591 8.89 25000 0.4611 1.2754
0.5399 9.24 26000 0.4645 1.3143
0.5875 9.6 27000 0.4383 1.0949
0.5281 9.95 28000 0.4252 1.0851
0.4801 10.31 29000 0.4065 1.1674
0.4978 10.66 30000 0.3869 1.0382
0.2993 11.02 31000 0.3862 1.0100
0.3392 11.38 32000 0.3657 0.9267
0.4248 11.73 33000 0.3800 0.8588
0.2666 12.09 34000 0.3458 0.9895
0.3525 12.44 35000 0.3649 0.8927
0.259 12.8 36000 0.3272 0.9232
0.2105 13.15 37000 0.3358 0.7679
0.2125 13.51 38000 0.3291 0.8509
0.2744 13.86 39000 0.3367 0.7735
0.1858 14.22 40000 0.3005 0.7237
0.1762 14.58 41000 0.3238 0.7320
0.2107 14.93 42000 0.3035 0.8229
0.1403 15.29 43000 0.2981 0.8188
0.124 15.64 44000 0.3082 0.8104
0.1398 16.0 45000 0.2967 0.8586
0.1207 16.35 46000 0.2838 0.9125
0.1422 16.71 47000 0.3029 0.9329
0.0779 17.06 48000 0.3022 0.7960
0.1103 17.42 49000 0.2900 0.8678
0.1011 17.77 50000 0.2931 0.7747
0.0883 18.13 51000 0.2722 0.7624
0.0468 18.49 52000 0.2826 0.7573
0.0782 18.84 53000 0.2745 0.8906
0.0558 19.2 54000 0.2756 0.7796
0.0792 19.55 55000 0.2799 0.8554
0.063 19.91 56000 0.2916 0.8130
0.0464 20.26 57000 0.2889 0.9519
0.058 20.62 58000 0.2719 0.7782
0.062 20.97 59000 0.2697 0.8140
0.038 21.33 60000 0.2876 0.7488
0.0436 21.69 61000 0.2776 0.7391
0.0363 22.04 62000 0.2730 0.8416
0.0406 22.4 63000 0.2852 0.8974
0.0268 22.75 64000 0.2818 0.9051
0.0143 23.11 65000 0.2733 0.8073
0.0274 23.46 66000 0.2694 0.9573
0.0233 23.82 67000 0.2705 0.8856
0.0177 24.17 68000 0.2701 0.8605
0.0237 24.53 69000 0.2683 0.7962
0.0247 24.88 70000 0.2717 0.8272
0.0135 25.24 71000 0.2737 0.8667
0.0169 25.6 72000 0.2739 0.8405
0.0173 25.95 73000 0.2685 0.7505
0.0168 26.31 74000 0.2682 0.9736
0.0179 26.66 75000 0.2644 0.8753
0.0114 27.02 76000 0.2749 0.8917
0.0121 27.37 77000 0.2733 0.9144
0.0145 27.73 78000 0.2637 0.8889
0.0131 28.08 79000 0.2693 0.9278
0.0078 28.44 80000 0.2669 0.9077
0.0129 28.79 81000 0.2665 0.9218
0.0215 29.15 82000 0.2509 0.7342
0.0291 29.51 83000 0.2573 0.7706
0.0233 29.86 84000 0.2516 0.7602
0.0305 30.22 85000 0.2839 1.0254
0.0424 30.57 86000 0.2725 0.8747
0.0346 30.93 87000 0.2725 0.8864
0.0212 31.28 88000 0.2746 0.8550
0.0266 31.64 89000 0.2834 0.8797
0.0255 31.99 90000 0.2687 0.7178
0.019 32.35 91000 0.2744 0.8784
0.0151 32.71 92000 0.2494 0.6553
0.0243 33.06 93000 0.2531 0.7540
0.04 33.42 94000 0.2526 0.8605
0.0307 33.77 95000 0.2597 0.8507
0.0258 34.13 96000 0.2714 0.7760
0.0245 34.48 97000 0.2343 0.6859

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.1