File size: 29,079 Bytes
621eff8 4edaa6a 621eff8 4edaa6a 1a613c5 4edaa6a 1a613c5 f0004a7 1a613c5 f0004a7 1a613c5 4edaa6a 1a613c5 4edaa6a f0004a7 1a613c5 f0004a7 4edaa6a f0004a7 4edaa6a f0004a7 4edaa6a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-wrinkle
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-wrinkle
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the AmirGenAI/my-wrinkle-seg-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0170
- Mean Iou: 0.1948
- Mean Accuracy: 0.3896
- Overall Accuracy: 0.3896
- Accuracy Unlabeled: nan
- Accuracy Wrinkle: 0.3896
- Iou Unlabeled: 0.0
- Iou Wrinkle: 0.3896
## 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: 6e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy Unlabeled | Accuracy Wrinkle | Iou Unlabeled | Iou Wrinkle | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|:-------------:|:------:|:----:|:------------------:|:----------------:|:-------------:|:-----------:|:---------------:|:-------------:|:--------:|:----------------:|
| 0.0207 | 0.0617 | 20 | nan | 0.3445 | 0.0 | 0.3445 | 0.0224 | 0.3445 | 0.1723 | 0.3445 |
| 0.0235 | 0.1235 | 40 | nan | 0.3026 | 0.0 | 0.3026 | 0.0214 | 0.3026 | 0.1513 | 0.3026 |
| 0.0159 | 0.1852 | 60 | nan | 0.3031 | 0.0 | 0.3031 | 0.0206 | 0.3031 | 0.1515 | 0.3031 |
| 0.0214 | 0.2469 | 80 | nan | 0.2827 | 0.0 | 0.2827 | 0.0204 | 0.2827 | 0.1413 | 0.2827 |
| 0.0204 | 0.3086 | 100 | nan | 0.2768 | 0.0 | 0.2768 | 0.0202 | 0.2768 | 0.1384 | 0.2768 |
| 0.0231 | 0.3704 | 120 | nan | 0.2846 | 0.0 | 0.2846 | 0.0200 | 0.2846 | 0.1423 | 0.2846 |
| 0.0151 | 0.4321 | 140 | nan | 0.2569 | 0.0 | 0.2569 | 0.0199 | 0.2569 | 0.1285 | 0.2569 |
| 0.0245 | 0.4938 | 160 | nan | 0.2752 | 0.0 | 0.2752 | 0.0197 | 0.2752 | 0.1376 | 0.2752 |
| 0.023 | 0.5556 | 180 | nan | 0.3322 | 0.0 | 0.3322 | 0.0200 | 0.3322 | 0.1661 | 0.3322 |
| 0.0321 | 0.6173 | 200 | nan | 0.3031 | 0.0 | 0.3031 | 0.0198 | 0.3031 | 0.1516 | 0.3031 |
| 0.0197 | 0.6790 | 220 | nan | 0.3157 | 0.0 | 0.3157 | 0.0196 | 0.3157 | 0.1578 | 0.3157 |
| 0.0192 | 0.7407 | 240 | nan | 0.3430 | 0.0 | 0.3430 | 0.0198 | 0.3430 | 0.1715 | 0.3430 |
| 0.0228 | 0.8025 | 260 | nan | 0.3144 | 0.0 | 0.3144 | 0.0194 | 0.3144 | 0.1572 | 0.3144 |
| 0.0216 | 0.8642 | 280 | nan | 0.3338 | 0.0 | 0.3338 | 0.0194 | 0.3338 | 0.1669 | 0.3338 |
| 0.0186 | 0.9259 | 300 | nan | 0.3411 | 0.0 | 0.3411 | 0.0193 | 0.3411 | 0.1705 | 0.3411 |
| 0.0202 | 0.9877 | 320 | nan | 0.3215 | 0.0 | 0.3215 | 0.0192 | 0.3215 | 0.1608 | 0.3215 |
| 0.0194 | 1.0494 | 340 | nan | 0.3286 | 0.0 | 0.3286 | 0.0194 | 0.3286 | 0.1643 | 0.3286 |
| 0.0166 | 1.1111 | 360 | nan | 0.2658 | 0.0 | 0.2658 | 0.0193 | 0.2658 | 0.1329 | 0.2658 |
| 0.0187 | 1.1728 | 380 | nan | 0.3133 | 0.0 | 0.3133 | 0.0191 | 0.3133 | 0.1567 | 0.3133 |
| 0.0208 | 1.2346 | 400 | nan | 0.3254 | 0.0 | 0.3254 | 0.0190 | 0.3254 | 0.1627 | 0.3254 |
| 0.0237 | 1.2963 | 420 | nan | 0.3093 | 0.0 | 0.3093 | 0.0190 | 0.3093 | 0.1546 | 0.3093 |
| 0.0244 | 1.3580 | 440 | nan | 0.3167 | 0.0 | 0.3167 | 0.0189 | 0.3167 | 0.1583 | 0.3167 |
| 0.0156 | 1.4198 | 460 | nan | 0.3408 | 0.0 | 0.3408 | 0.0191 | 0.3408 | 0.1704 | 0.3408 |
| 0.0231 | 1.4815 | 480 | nan | 0.2716 | 0.0 | 0.2716 | 0.0190 | 0.2716 | 0.1358 | 0.2716 |
| 0.0197 | 1.5432 | 500 | nan | 0.3540 | 0.0 | 0.3540 | 0.0190 | 0.3540 | 0.1770 | 0.3540 |
| 0.013 | 1.6049 | 520 | nan | 0.3358 | 0.0 | 0.3358 | 0.0188 | 0.3358 | 0.1679 | 0.3358 |
| 0.0158 | 1.6667 | 540 | nan | 0.3619 | 0.0 | 0.3619 | 0.0188 | 0.3619 | 0.1810 | 0.3619 |
| 0.0212 | 1.7284 | 560 | nan | 0.3649 | 0.0 | 0.3649 | 0.0188 | 0.3649 | 0.1824 | 0.3649 |
| 0.0231 | 1.7901 | 580 | nan | 0.2906 | 0.0 | 0.2906 | 0.0189 | 0.2906 | 0.1453 | 0.2906 |
| 0.0158 | 1.8519 | 600 | nan | 0.3210 | 0.0 | 0.3210 | 0.0187 | 0.3210 | 0.1605 | 0.3210 |
| 0.024 | 1.9136 | 620 | nan | 0.3117 | 0.0 | 0.3117 | 0.0187 | 0.3117 | 0.1558 | 0.3117 |
| 0.0193 | 1.9753 | 640 | nan | 0.3596 | 0.0 | 0.3596 | 0.0187 | 0.3596 | 0.1798 | 0.3596 |
| 0.0201 | 2.0370 | 660 | nan | 0.3628 | 0.0 | 0.3628 | 0.0187 | 0.3628 | 0.1814 | 0.3628 |
| 0.0162 | 2.0988 | 680 | nan | 0.3238 | 0.0 | 0.3238 | 0.0185 | 0.3238 | 0.1619 | 0.3238 |
| 0.0219 | 2.1605 | 700 | nan | 0.3622 | 0.0 | 0.3622 | 0.0186 | 0.3622 | 0.1811 | 0.3622 |
| 0.0207 | 2.2222 | 720 | nan | 0.3919 | 0.0 | 0.3919 | 0.0189 | 0.3919 | 0.1959 | 0.3919 |
| 0.0176 | 2.2840 | 740 | nan | 0.3116 | 0.0 | 0.3116 | 0.0185 | 0.3116 | 0.1558 | 0.3116 |
| 0.0149 | 2.3457 | 760 | nan | 0.3917 | 0.0 | 0.3917 | 0.0186 | 0.3917 | 0.1959 | 0.3917 |
| 0.0192 | 2.4074 | 780 | nan | 0.3097 | 0.0 | 0.3097 | 0.0184 | 0.3097 | 0.1548 | 0.3097 |
| 0.0202 | 2.4691 | 800 | nan | 0.3745 | 0.0 | 0.3745 | 0.0183 | 0.3745 | 0.1872 | 0.3745 |
| 0.0181 | 2.5309 | 820 | nan | 0.3687 | 0.0 | 0.3687 | 0.0185 | 0.3687 | 0.1844 | 0.3687 |
| 0.0168 | 2.5926 | 840 | nan | 0.3629 | 0.0 | 0.3629 | 0.0183 | 0.3629 | 0.1815 | 0.3629 |
| 0.0171 | 2.6543 | 860 | nan | 0.3148 | 0.0 | 0.3148 | 0.0183 | 0.3148 | 0.1574 | 0.3148 |
| 0.0166 | 2.7160 | 880 | nan | 0.3426 | 0.0 | 0.3426 | 0.0182 | 0.3426 | 0.1713 | 0.3426 |
| 0.0123 | 2.7778 | 900 | nan | 0.3670 | 0.0 | 0.3670 | 0.0183 | 0.3670 | 0.1835 | 0.3670 |
| 0.0149 | 2.8395 | 920 | nan | 0.3535 | 0.0 | 0.3535 | 0.0182 | 0.3535 | 0.1767 | 0.3535 |
| 0.016 | 2.9012 | 940 | nan | 0.3856 | 0.0 | 0.3856 | 0.0183 | 0.3856 | 0.1928 | 0.3856 |
| 0.0192 | 2.9630 | 960 | nan | 0.3647 | 0.0 | 0.3647 | 0.0182 | 0.3647 | 0.1823 | 0.3647 |
| 0.0188 | 3.0247 | 980 | nan | 0.3313 | 0.0 | 0.3313 | 0.0182 | 0.3313 | 0.1657 | 0.3313 |
| 0.0203 | 3.0864 | 1000 | nan | 0.3233 | 0.0 | 0.3233 | 0.0181 | 0.3233 | 0.1617 | 0.3233 |
| 0.0152 | 3.1481 | 1020 | nan | 0.3047 | 0.0 | 0.3047 | 0.0182 | 0.3047 | 0.1523 | 0.3047 |
| 0.0175 | 3.2099 | 1040 | nan | 0.3545 | 0.0 | 0.3545 | 0.0181 | 0.3545 | 0.1772 | 0.3545 |
| 0.0241 | 3.2716 | 1060 | nan | 0.3898 | 0.0 | 0.3898 | 0.0182 | 0.3898 | 0.1949 | 0.3898 |
| 0.0215 | 3.3333 | 1080 | nan | 0.3102 | 0.0 | 0.3102 | 0.0181 | 0.3102 | 0.1551 | 0.3102 |
| 0.0205 | 3.3951 | 1100 | nan | 0.3581 | 0.0 | 0.3581 | 0.0180 | 0.3581 | 0.1791 | 0.3581 |
| 0.0208 | 3.4568 | 1120 | nan | 0.3611 | 0.0 | 0.3611 | 0.0179 | 0.3611 | 0.1805 | 0.3611 |
| 0.0184 | 3.5185 | 1140 | nan | 0.3151 | 0.0 | 0.3151 | 0.0180 | 0.3151 | 0.1576 | 0.3151 |
| 0.0159 | 3.5802 | 1160 | nan | 0.4039 | 0.0 | 0.4039 | 0.0182 | 0.4039 | 0.2019 | 0.4039 |
| 0.0219 | 3.6420 | 1180 | nan | 0.3444 | 0.0 | 0.3444 | 0.0181 | 0.3444 | 0.1722 | 0.3444 |
| 0.023 | 3.7037 | 1200 | nan | 0.3265 | 0.0 | 0.3265 | 0.0179 | 0.3265 | 0.1633 | 0.3265 |
| 0.0148 | 3.7654 | 1220 | nan | 0.3677 | 0.0 | 0.3677 | 0.0179 | 0.3677 | 0.1838 | 0.3677 |
| 0.019 | 3.8272 | 1240 | nan | 0.3422 | 0.0 | 0.3422 | 0.0178 | 0.3422 | 0.1711 | 0.3422 |
| 0.0154 | 3.8889 | 1260 | nan | 0.3611 | 0.0 | 0.3611 | 0.0179 | 0.3611 | 0.1806 | 0.3611 |
| 0.0212 | 3.9506 | 1280 | nan | 0.3274 | 0.0 | 0.3274 | 0.0178 | 0.3274 | 0.1637 | 0.3274 |
| 0.0192 | 4.0123 | 1300 | nan | 0.3829 | 0.0 | 0.3829 | 0.0178 | 0.3829 | 0.1914 | 0.3829 |
| 0.0183 | 4.0741 | 1320 | nan | 0.4120 | 0.0 | 0.4120 | 0.0180 | 0.4120 | 0.2060 | 0.4120 |
| 0.0141 | 4.1358 | 1340 | nan | 0.3613 | 0.0 | 0.3613 | 0.0177 | 0.3613 | 0.1806 | 0.3613 |
| 0.0165 | 4.1975 | 1360 | nan | 0.3552 | 0.0 | 0.3552 | 0.0180 | 0.3552 | 0.1776 | 0.3552 |
| 0.018 | 4.2593 | 1380 | nan | 0.3646 | 0.0 | 0.3646 | 0.0177 | 0.3646 | 0.1823 | 0.3646 |
| 0.0166 | 4.3210 | 1400 | nan | 0.3557 | 0.0 | 0.3557 | 0.0177 | 0.3557 | 0.1778 | 0.3557 |
| 0.0193 | 4.3827 | 1420 | nan | 0.3518 | 0.0 | 0.3518 | 0.0177 | 0.3518 | 0.1759 | 0.3518 |
| 0.0173 | 4.4444 | 1440 | nan | 0.3640 | 0.0 | 0.3640 | 0.0176 | 0.3640 | 0.1820 | 0.3640 |
| 0.0159 | 4.5062 | 1460 | nan | 0.4083 | 0.0 | 0.4083 | 0.0178 | 0.4083 | 0.2042 | 0.4083 |
| 0.0186 | 4.5679 | 1480 | nan | 0.3679 | 0.0 | 0.3679 | 0.0177 | 0.3679 | 0.1839 | 0.3679 |
| 0.0214 | 4.6296 | 1500 | nan | 0.3567 | 0.0 | 0.3567 | 0.0176 | 0.3567 | 0.1783 | 0.3567 |
| 0.0192 | 4.6914 | 1520 | nan | 0.3595 | 0.0 | 0.3595 | 0.0176 | 0.3595 | 0.1797 | 0.3595 |
| 0.0173 | 4.7531 | 1540 | nan | 0.3567 | 0.0 | 0.3567 | 0.0176 | 0.3567 | 0.1784 | 0.3567 |
| 0.0185 | 4.8148 | 1560 | nan | 0.3830 | 0.0 | 0.3830 | 0.0176 | 0.3830 | 0.1915 | 0.3830 |
| 0.0179 | 4.8765 | 1580 | nan | 0.3851 | 0.0 | 0.3851 | 0.0176 | 0.3851 | 0.1926 | 0.3851 |
| 0.0152 | 4.9383 | 1600 | nan | 0.4017 | 0.0 | 0.4017 | 0.0177 | 0.4017 | 0.2009 | 0.4017 |
| 0.0281 | 5.0 | 1620 | nan | 0.3503 | 0.0 | 0.3503 | 0.0176 | 0.3503 | 0.1752 | 0.3503 |
| 0.0185 | 5.0617 | 1640 | nan | 0.4148 | 0.0 | 0.4148 | 0.0178 | 0.4148 | 0.2074 | 0.4148 |
| 0.0209 | 5.1235 | 1660 | nan | 0.3782 | 0.0 | 0.3782 | 0.0176 | 0.3782 | 0.1891 | 0.3782 |
| 0.0191 | 5.1852 | 1680 | nan | 0.3441 | 0.0 | 0.3441 | 0.0175 | 0.3441 | 0.1720 | 0.3441 |
| 0.0134 | 5.2469 | 1700 | nan | 0.3189 | 0.0 | 0.3189 | 0.0176 | 0.3189 | 0.1594 | 0.3189 |
| 0.0178 | 5.3086 | 1720 | nan | 0.3452 | 0.0 | 0.3452 | 0.0176 | 0.3452 | 0.1726 | 0.3452 |
| 0.0214 | 5.3704 | 1740 | nan | 0.4143 | 0.0 | 0.4143 | 0.0177 | 0.4143 | 0.2072 | 0.4143 |
| 0.0179 | 5.4321 | 1760 | nan | 0.3732 | 0.0 | 0.3732 | 0.0175 | 0.3732 | 0.1866 | 0.3732 |
| 0.0187 | 5.4938 | 1780 | nan | 0.3843 | 0.0 | 0.3843 | 0.0176 | 0.3843 | 0.1921 | 0.3843 |
| 0.0218 | 5.5556 | 1800 | nan | 0.3807 | 0.0 | 0.3807 | 0.0175 | 0.3807 | 0.1904 | 0.3807 |
| 0.0154 | 5.6173 | 1820 | nan | 0.3602 | 0.0 | 0.3602 | 0.0175 | 0.3602 | 0.1801 | 0.3602 |
| 0.021 | 5.6790 | 1840 | nan | 0.3445 | 0.0 | 0.3445 | 0.0175 | 0.3445 | 0.1723 | 0.3445 |
| 0.0191 | 5.7407 | 1860 | nan | 0.3290 | 0.0 | 0.3290 | 0.0175 | 0.3290 | 0.1645 | 0.3290 |
| 0.0194 | 5.8025 | 1880 | nan | 0.3564 | 0.0 | 0.3564 | 0.0174 | 0.3564 | 0.1782 | 0.3564 |
| 0.0121 | 5.8642 | 1900 | nan | 0.3860 | 0.0 | 0.3860 | 0.0175 | 0.3860 | 0.1930 | 0.3860 |
| 0.0194 | 5.9259 | 1920 | nan | 0.4023 | 0.0 | 0.4023 | 0.0175 | 0.4023 | 0.2011 | 0.4023 |
| 0.0159 | 5.9877 | 1940 | nan | 0.3953 | 0.0 | 0.3953 | 0.0173 | 0.3953 | 0.1976 | 0.3953 |
| 0.0142 | 6.0494 | 1960 | nan | 0.4036 | 0.0 | 0.4036 | 0.0173 | 0.4036 | 0.2018 | 0.4036 |
| 0.0165 | 6.1111 | 1980 | nan | 0.3593 | 0.0 | 0.3593 | 0.0173 | 0.3593 | 0.1796 | 0.3593 |
| 0.0166 | 6.1728 | 2000 | nan | 0.4104 | 0.0 | 0.4104 | 0.0174 | 0.4104 | 0.2052 | 0.4104 |
| 0.0164 | 6.2346 | 2020 | nan | 0.3653 | 0.0 | 0.3653 | 0.0174 | 0.3653 | 0.1826 | 0.3653 |
| 0.0267 | 6.2963 | 2040 | nan | 0.3582 | 0.0 | 0.3582 | 0.0173 | 0.3582 | 0.1791 | 0.3582 |
| 0.0202 | 6.3580 | 2060 | nan | 0.3613 | 0.0 | 0.3613 | 0.0173 | 0.3613 | 0.1806 | 0.3613 |
| 0.0134 | 6.4198 | 2080 | nan | 0.4004 | 0.0 | 0.4004 | 0.0174 | 0.4004 | 0.2002 | 0.4004 |
| 0.014 | 6.4815 | 2100 | nan | 0.3781 | 0.0 | 0.3781 | 0.0173 | 0.3781 | 0.1890 | 0.3781 |
| 0.0197 | 6.5432 | 2120 | nan | 0.3724 | 0.0 | 0.3724 | 0.0173 | 0.3724 | 0.1862 | 0.3724 |
| 0.0185 | 6.6049 | 2140 | nan | 0.3306 | 0.0 | 0.3306 | 0.0174 | 0.3306 | 0.1653 | 0.3306 |
| 0.0205 | 6.6667 | 2160 | nan | 0.3523 | 0.0 | 0.3523 | 0.0173 | 0.3523 | 0.1761 | 0.3523 |
| 0.0152 | 6.7284 | 2180 | nan | 0.3690 | 0.0 | 0.3690 | 0.0172 | 0.3690 | 0.1845 | 0.3690 |
| 0.0158 | 6.7901 | 2200 | 0.0172 | 0.1793 | 0.3586 | 0.3586 | nan | 0.3586 | 0.0 | 0.3586 |
| 0.0199 | 6.8519 | 2220 | 0.0174 | 0.2063 | 0.4126 | 0.4126 | nan | 0.4126 | 0.0 | 0.4126 |
| 0.0186 | 6.9136 | 2240 | 0.0173 | 0.1691 | 0.3382 | 0.3382 | nan | 0.3382 | 0.0 | 0.3382 |
| 0.0228 | 6.9753 | 2260 | 0.0173 | 0.1804 | 0.3609 | 0.3609 | nan | 0.3609 | 0.0 | 0.3609 |
| 0.0193 | 7.0370 | 2280 | 0.0172 | 0.1887 | 0.3775 | 0.3775 | nan | 0.3775 | 0.0 | 0.3775 |
| 0.0161 | 7.0988 | 2300 | 0.0172 | 0.1900 | 0.3799 | 0.3799 | nan | 0.3799 | 0.0 | 0.3799 |
| 0.0182 | 7.1605 | 2320 | 0.0172 | 0.1888 | 0.3775 | 0.3775 | nan | 0.3775 | 0.0 | 0.3775 |
| 0.0165 | 7.2222 | 2340 | 0.0172 | 0.2037 | 0.4075 | 0.4075 | nan | 0.4075 | 0.0 | 0.4075 |
| 0.0197 | 7.2840 | 2360 | 0.0172 | 0.1900 | 0.3800 | 0.3800 | nan | 0.3800 | 0.0 | 0.3800 |
| 0.0246 | 7.3457 | 2380 | 0.0172 | 0.1861 | 0.3722 | 0.3722 | nan | 0.3722 | 0.0 | 0.3722 |
| 0.0191 | 7.4074 | 2400 | 0.0172 | 0.1935 | 0.3870 | 0.3870 | nan | 0.3870 | 0.0 | 0.3870 |
| 0.0157 | 7.4691 | 2420 | 0.0172 | 0.1913 | 0.3826 | 0.3826 | nan | 0.3826 | 0.0 | 0.3826 |
| 0.0187 | 7.5309 | 2440 | 0.0172 | 0.1863 | 0.3727 | 0.3727 | nan | 0.3727 | 0.0 | 0.3727 |
| 0.0199 | 7.5926 | 2460 | 0.0172 | 0.1817 | 0.3634 | 0.3634 | nan | 0.3634 | 0.0 | 0.3634 |
| 0.0226 | 7.6543 | 2480 | 0.0172 | 0.1944 | 0.3889 | 0.3889 | nan | 0.3889 | 0.0 | 0.3889 |
| 0.0167 | 7.7160 | 2500 | 0.0172 | 0.1833 | 0.3666 | 0.3666 | nan | 0.3666 | 0.0 | 0.3666 |
| 0.017 | 7.7778 | 2520 | 0.0171 | 0.1924 | 0.3848 | 0.3848 | nan | 0.3848 | 0.0 | 0.3848 |
| 0.0219 | 7.8395 | 2540 | 0.0171 | 0.1885 | 0.3769 | 0.3769 | nan | 0.3769 | 0.0 | 0.3769 |
| 0.0149 | 7.9012 | 2560 | 0.0172 | 0.2088 | 0.4177 | 0.4177 | nan | 0.4177 | 0.0 | 0.4177 |
| 0.0137 | 7.9630 | 2580 | 0.0171 | 0.1932 | 0.3865 | 0.3865 | nan | 0.3865 | 0.0 | 0.3865 |
| 0.0235 | 8.0247 | 2600 | 0.0171 | 0.1837 | 0.3674 | 0.3674 | nan | 0.3674 | 0.0 | 0.3674 |
| 0.0169 | 8.0864 | 2620 | 0.0171 | 0.2023 | 0.4046 | 0.4046 | nan | 0.4046 | 0.0 | 0.4046 |
| 0.0183 | 8.1481 | 2640 | 0.0171 | 0.1876 | 0.3753 | 0.3753 | nan | 0.3753 | 0.0 | 0.3753 |
| 0.0171 | 8.2099 | 2660 | 0.0171 | 0.1876 | 0.3753 | 0.3753 | nan | 0.3753 | 0.0 | 0.3753 |
| 0.0202 | 8.2716 | 2680 | 0.0172 | 0.2087 | 0.4173 | 0.4173 | nan | 0.4173 | 0.0 | 0.4173 |
| 0.0229 | 8.3333 | 2700 | 0.0171 | 0.1799 | 0.3599 | 0.3599 | nan | 0.3599 | 0.0 | 0.3599 |
| 0.0138 | 8.3951 | 2720 | 0.0171 | 0.1941 | 0.3882 | 0.3882 | nan | 0.3882 | 0.0 | 0.3882 |
| 0.0155 | 8.4568 | 2740 | 0.0171 | 0.2031 | 0.4063 | 0.4063 | nan | 0.4063 | 0.0 | 0.4063 |
| 0.0171 | 8.5185 | 2760 | 0.0171 | 0.2038 | 0.4075 | 0.4075 | nan | 0.4075 | 0.0 | 0.4075 |
| 0.0211 | 8.5802 | 2780 | 0.0170 | 0.1946 | 0.3893 | 0.3893 | nan | 0.3893 | 0.0 | 0.3893 |
| 0.0161 | 8.6420 | 2800 | 0.0170 | 0.1950 | 0.3900 | 0.3900 | nan | 0.3900 | 0.0 | 0.3900 |
| 0.0175 | 8.7037 | 2820 | 0.0171 | 0.1946 | 0.3893 | 0.3893 | nan | 0.3893 | 0.0 | 0.3893 |
| 0.0199 | 8.7654 | 2840 | 0.0171 | 0.1948 | 0.3895 | 0.3895 | nan | 0.3895 | 0.0 | 0.3895 |
| 0.0169 | 8.8272 | 2860 | 0.0171 | 0.2013 | 0.4027 | 0.4027 | nan | 0.4027 | 0.0 | 0.4027 |
| 0.0213 | 8.8889 | 2880 | 0.0170 | 0.1964 | 0.3927 | 0.3927 | nan | 0.3927 | 0.0 | 0.3927 |
| 0.018 | 8.9506 | 2900 | 0.0171 | 0.1850 | 0.3700 | 0.3700 | nan | 0.3700 | 0.0 | 0.3700 |
| 0.0195 | 9.0123 | 2920 | 0.0171 | 0.2041 | 0.4081 | 0.4081 | nan | 0.4081 | 0.0 | 0.4081 |
| 0.0156 | 9.0741 | 2940 | 0.0171 | 0.1922 | 0.3844 | 0.3844 | nan | 0.3844 | 0.0 | 0.3844 |
| 0.0201 | 9.1358 | 2960 | 0.0171 | 0.1985 | 0.3971 | 0.3971 | nan | 0.3971 | 0.0 | 0.3971 |
| 0.0223 | 9.1975 | 2980 | 0.0171 | 0.1970 | 0.3940 | 0.3940 | nan | 0.3940 | 0.0 | 0.3940 |
| 0.0193 | 9.2593 | 3000 | 0.0170 | 0.1918 | 0.3836 | 0.3836 | nan | 0.3836 | 0.0 | 0.3836 |
| 0.0121 | 9.3210 | 3020 | 0.0170 | 0.1978 | 0.3956 | 0.3956 | nan | 0.3956 | 0.0 | 0.3956 |
| 0.0183 | 9.3827 | 3040 | 0.0170 | 0.1922 | 0.3845 | 0.3845 | nan | 0.3845 | 0.0 | 0.3845 |
| 0.0208 | 9.4444 | 3060 | 0.0170 | 0.1966 | 0.3932 | 0.3932 | nan | 0.3932 | 0.0 | 0.3932 |
| 0.0207 | 9.5062 | 3080 | 0.0170 | 0.1967 | 0.3933 | 0.3933 | nan | 0.3933 | 0.0 | 0.3933 |
| 0.0194 | 9.5679 | 3100 | 0.0170 | 0.1922 | 0.3843 | 0.3843 | nan | 0.3843 | 0.0 | 0.3843 |
| 0.0158 | 9.6296 | 3120 | 0.0170 | 0.1965 | 0.3929 | 0.3929 | nan | 0.3929 | 0.0 | 0.3929 |
| 0.0158 | 9.6914 | 3140 | 0.0170 | 0.1898 | 0.3796 | 0.3796 | nan | 0.3796 | 0.0 | 0.3796 |
| 0.0215 | 9.7531 | 3160 | 0.0170 | 0.1922 | 0.3845 | 0.3845 | nan | 0.3845 | 0.0 | 0.3845 |
| 0.0143 | 9.8148 | 3180 | 0.0170 | 0.1989 | 0.3979 | 0.3979 | nan | 0.3979 | 0.0 | 0.3979 |
| 0.0177 | 9.8765 | 3200 | 0.0170 | 0.1935 | 0.3870 | 0.3870 | nan | 0.3870 | 0.0 | 0.3870 |
| 0.0159 | 9.9383 | 3220 | 0.0170 | 0.1911 | 0.3822 | 0.3822 | nan | 0.3822 | 0.0 | 0.3822 |
| 0.0189 | 10.0 | 3240 | 0.0170 | 0.1948 | 0.3896 | 0.3896 | nan | 0.3896 | 0.0 | 0.3896 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
|