--- tags: - generated_from_trainer model-index: - name: TrOCR-SIN results: [] --- # TrOCR-SIN This model is a fine-tuned version of [](https://huggingface.co./) 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