File size: 23,908 Bytes
4fe4c88 4ce2991 4fe4c88 4ce2991 4fe4c88 |
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 |
---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5
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. -->
# INTERNAL_BEST-safety-utcustom-train-SF-RGB-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0472
- Mean Iou: 0.8728
- Mean Accuracy: 0.9195
- Overall Accuracy: 0.9919
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.8426
- Accuracy Unsafe: 0.9964
- Iou Unlabeled: nan
- Iou Safe: 0.7540
- Iou Unsafe: 0.9917
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.2199 | 2.0 | 20 | 1.1474 | 0.0765 | 0.4399 | 0.2168 | nan | 0.6770 | 0.2028 | 0.0 | 0.0279 | 0.2014 |
| 1.1542 | 4.0 | 40 | 1.0616 | 0.1558 | 0.6082 | 0.4365 | nan | 0.7908 | 0.4257 | 0.0 | 0.0436 | 0.4237 |
| 1.0324 | 6.0 | 60 | 0.9140 | 0.2569 | 0.6886 | 0.7091 | nan | 0.6667 | 0.7104 | 0.0 | 0.0672 | 0.7036 |
| 0.8058 | 8.0 | 80 | 0.7629 | 0.2970 | 0.6780 | 0.8115 | nan | 0.5360 | 0.8199 | 0.0 | 0.0823 | 0.8086 |
| 0.681 | 10.0 | 100 | 0.5545 | 0.3510 | 0.6964 | 0.9135 | nan | 0.4657 | 0.9271 | 0.0 | 0.1407 | 0.9123 |
| 0.5248 | 12.0 | 120 | 0.4153 | 0.3738 | 0.6747 | 0.9462 | nan | 0.3861 | 0.9633 | 0.0 | 0.1757 | 0.9456 |
| 0.3372 | 14.0 | 140 | 0.3050 | 0.3896 | 0.6548 | 0.9628 | nan | 0.3276 | 0.9821 | 0.0 | 0.2064 | 0.9624 |
| 0.2818 | 16.0 | 160 | 0.2346 | 0.4313 | 0.7331 | 0.9703 | nan | 0.4810 | 0.9852 | 0.0 | 0.3239 | 0.9699 |
| 0.2081 | 18.0 | 180 | 0.1802 | 0.4762 | 0.7973 | 0.9782 | nan | 0.6050 | 0.9896 | 0.0 | 0.4509 | 0.9778 |
| 0.141 | 20.0 | 200 | 0.1362 | 0.4968 | 0.7945 | 0.9830 | nan | 0.5943 | 0.9948 | 0.0 | 0.5078 | 0.9827 |
| 0.0963 | 22.0 | 220 | 0.1032 | 0.7409 | 0.7652 | 0.9841 | nan | 0.5326 | 0.9979 | nan | 0.4978 | 0.9839 |
| 0.0842 | 24.0 | 240 | 0.0770 | 0.7840 | 0.8200 | 0.9863 | nan | 0.6432 | 0.9968 | nan | 0.5818 | 0.9861 |
| 0.0702 | 26.0 | 260 | 0.0669 | 0.7836 | 0.8193 | 0.9863 | nan | 0.6417 | 0.9968 | nan | 0.5812 | 0.9861 |
| 0.0706 | 28.0 | 280 | 0.0671 | 0.8065 | 0.8593 | 0.9872 | nan | 0.7234 | 0.9953 | nan | 0.6261 | 0.9870 |
| 0.0747 | 30.0 | 300 | 0.0551 | 0.7808 | 0.7980 | 0.9870 | nan | 0.5971 | 0.9988 | nan | 0.5748 | 0.9867 |
| 0.057 | 32.0 | 320 | 0.0492 | 0.8267 | 0.8736 | 0.9888 | nan | 0.7511 | 0.9961 | nan | 0.6648 | 0.9886 |
| 0.0435 | 34.0 | 340 | 0.0507 | 0.7956 | 0.8134 | 0.9878 | nan | 0.6280 | 0.9988 | nan | 0.6035 | 0.9876 |
| 0.0326 | 36.0 | 360 | 0.0418 | 0.8422 | 0.8895 | 0.9898 | nan | 0.7830 | 0.9961 | nan | 0.6947 | 0.9896 |
| 0.0262 | 38.0 | 380 | 0.0420 | 0.8280 | 0.8550 | 0.9895 | nan | 0.7120 | 0.9979 | nan | 0.6667 | 0.9893 |
| 0.0268 | 40.0 | 400 | 0.0392 | 0.8407 | 0.8822 | 0.9899 | nan | 0.7676 | 0.9967 | nan | 0.6918 | 0.9897 |
| 0.0395 | 42.0 | 420 | 0.0466 | 0.5436 | 0.8370 | 0.9889 | nan | 0.6755 | 0.9984 | 0.0 | 0.6422 | 0.9887 |
| 0.0279 | 44.0 | 440 | 0.0439 | 0.8321 | 0.8946 | 0.9887 | nan | 0.7945 | 0.9947 | nan | 0.6758 | 0.9885 |
| 0.0468 | 46.0 | 460 | 0.0360 | 0.8480 | 0.8894 | 0.9904 | nan | 0.7822 | 0.9967 | nan | 0.7059 | 0.9901 |
| 0.0233 | 48.0 | 480 | 0.0376 | 0.8507 | 0.8962 | 0.9905 | nan | 0.7960 | 0.9964 | nan | 0.7113 | 0.9902 |
| 0.0288 | 50.0 | 500 | 0.0386 | 0.8404 | 0.8845 | 0.9898 | nan | 0.7725 | 0.9964 | nan | 0.6913 | 0.9896 |
| 0.0266 | 52.0 | 520 | 0.0361 | 0.8455 | 0.8768 | 0.9905 | nan | 0.7560 | 0.9976 | nan | 0.7008 | 0.9902 |
| 0.0241 | 54.0 | 540 | 0.0367 | 0.8504 | 0.9039 | 0.9902 | nan | 0.8122 | 0.9957 | nan | 0.7109 | 0.9900 |
| 0.0239 | 56.0 | 560 | 0.0414 | 0.8401 | 0.8809 | 0.9899 | nan | 0.7650 | 0.9967 | nan | 0.6906 | 0.9896 |
| 0.0221 | 58.0 | 580 | 0.0375 | 0.8536 | 0.8971 | 0.9907 | nan | 0.7977 | 0.9966 | nan | 0.7167 | 0.9905 |
| 0.0324 | 60.0 | 600 | 0.0390 | 0.8566 | 0.9017 | 0.9908 | nan | 0.8069 | 0.9964 | nan | 0.7226 | 0.9906 |
| 0.0264 | 62.0 | 620 | 0.0405 | 0.8506 | 0.9088 | 0.9901 | nan | 0.8224 | 0.9952 | nan | 0.7114 | 0.9899 |
| 0.0146 | 64.0 | 640 | 0.0356 | 0.8627 | 0.9158 | 0.9911 | nan | 0.8358 | 0.9958 | nan | 0.7346 | 0.9909 |
| 0.0252 | 66.0 | 660 | 0.0310 | 0.8667 | 0.9010 | 0.9917 | nan | 0.8046 | 0.9974 | nan | 0.7418 | 0.9915 |
| 0.0155 | 68.0 | 680 | 0.0359 | 0.8567 | 0.9056 | 0.9908 | nan | 0.8152 | 0.9961 | nan | 0.7229 | 0.9905 |
| 0.0169 | 70.0 | 700 | 0.0490 | 0.8476 | 0.9182 | 0.9896 | nan | 0.8422 | 0.9941 | nan | 0.7058 | 0.9894 |
| 0.0142 | 72.0 | 720 | 0.0357 | 0.8442 | 0.8771 | 0.9903 | nan | 0.7568 | 0.9974 | nan | 0.6982 | 0.9901 |
| 0.0244 | 74.0 | 740 | 0.0400 | 0.8523 | 0.9070 | 0.9903 | nan | 0.8183 | 0.9956 | nan | 0.7146 | 0.9901 |
| 0.016 | 76.0 | 760 | 0.0302 | 0.8644 | 0.9037 | 0.9915 | nan | 0.8105 | 0.9970 | nan | 0.7376 | 0.9913 |
| 0.0137 | 78.0 | 780 | 0.0325 | 0.8664 | 0.9118 | 0.9915 | nan | 0.8271 | 0.9965 | nan | 0.7415 | 0.9913 |
| 0.0115 | 80.0 | 800 | 0.0347 | 0.8678 | 0.9162 | 0.9915 | nan | 0.8362 | 0.9962 | nan | 0.7443 | 0.9913 |
| 0.0117 | 82.0 | 820 | 0.0320 | 0.8697 | 0.9084 | 0.9918 | nan | 0.8197 | 0.9971 | nan | 0.7478 | 0.9916 |
| 0.0108 | 84.0 | 840 | 0.0348 | 0.8691 | 0.9192 | 0.9916 | nan | 0.8423 | 0.9961 | nan | 0.7468 | 0.9913 |
| 0.0101 | 86.0 | 860 | 0.0342 | 0.8683 | 0.9081 | 0.9917 | nan | 0.8192 | 0.9970 | nan | 0.7452 | 0.9915 |
| 0.0085 | 88.0 | 880 | 0.0441 | 0.8639 | 0.9214 | 0.9911 | nan | 0.8474 | 0.9954 | nan | 0.7370 | 0.9908 |
| 0.009 | 90.0 | 900 | 0.0428 | 0.8619 | 0.9086 | 0.9912 | nan | 0.8209 | 0.9963 | nan | 0.7330 | 0.9909 |
| 0.009 | 92.0 | 920 | 0.0444 | 0.8620 | 0.9089 | 0.9912 | nan | 0.8215 | 0.9963 | nan | 0.7331 | 0.9909 |
| 0.0089 | 94.0 | 940 | 0.0410 | 0.8645 | 0.9147 | 0.9913 | nan | 0.8334 | 0.9961 | nan | 0.7380 | 0.9910 |
| 0.0091 | 96.0 | 960 | 0.0418 | 0.8663 | 0.9155 | 0.9914 | nan | 0.8349 | 0.9962 | nan | 0.7413 | 0.9912 |
| 0.0079 | 98.0 | 980 | 0.0398 | 0.8629 | 0.9085 | 0.9913 | nan | 0.8205 | 0.9965 | nan | 0.7348 | 0.9910 |
| 0.0084 | 100.0 | 1000 | 0.0497 | 0.8553 | 0.9109 | 0.9905 | nan | 0.8262 | 0.9955 | nan | 0.7204 | 0.9903 |
| 0.0088 | 102.0 | 1020 | 0.0399 | 0.8558 | 0.9058 | 0.9907 | nan | 0.8156 | 0.9960 | nan | 0.7212 | 0.9905 |
| 0.0089 | 104.0 | 1040 | 0.0388 | 0.8678 | 0.9225 | 0.9914 | nan | 0.8494 | 0.9957 | nan | 0.7444 | 0.9912 |
| 0.008 | 106.0 | 1060 | 0.0449 | 0.8622 | 0.9225 | 0.9909 | nan | 0.8498 | 0.9952 | nan | 0.7337 | 0.9907 |
| 0.0084 | 108.0 | 1080 | 0.0429 | 0.8687 | 0.9233 | 0.9914 | nan | 0.8510 | 0.9957 | nan | 0.7462 | 0.9912 |
| 0.0084 | 110.0 | 1100 | 0.0405 | 0.8687 | 0.9169 | 0.9916 | nan | 0.8375 | 0.9963 | nan | 0.7460 | 0.9914 |
| 0.007 | 112.0 | 1120 | 0.0544 | 0.8620 | 0.9180 | 0.9910 | nan | 0.8404 | 0.9956 | nan | 0.7333 | 0.9907 |
| 0.0079 | 114.0 | 1140 | 0.0501 | 0.8602 | 0.9176 | 0.9908 | nan | 0.8399 | 0.9954 | nan | 0.7299 | 0.9906 |
| 0.0084 | 116.0 | 1160 | 0.0508 | 0.8605 | 0.9212 | 0.9908 | nan | 0.8473 | 0.9951 | nan | 0.7304 | 0.9905 |
| 0.0113 | 118.0 | 1180 | 0.0511 | 0.8601 | 0.9228 | 0.9907 | nan | 0.8507 | 0.9950 | nan | 0.7298 | 0.9905 |
| 0.0076 | 120.0 | 1200 | 0.0556 | 0.8602 | 0.9264 | 0.9906 | nan | 0.8582 | 0.9947 | nan | 0.7299 | 0.9904 |
| 0.0081 | 122.0 | 1220 | 0.0471 | 0.8665 | 0.9256 | 0.9912 | nan | 0.8559 | 0.9953 | nan | 0.7420 | 0.9910 |
| 0.0054 | 124.0 | 1240 | 0.0504 | 0.8652 | 0.9174 | 0.9913 | nan | 0.8389 | 0.9959 | nan | 0.7394 | 0.9910 |
| 0.0054 | 126.0 | 1260 | 0.0502 | 0.8666 | 0.9209 | 0.9913 | nan | 0.8461 | 0.9957 | nan | 0.7420 | 0.9911 |
| 0.0092 | 128.0 | 1280 | 0.0540 | 0.8642 | 0.9223 | 0.9911 | nan | 0.8492 | 0.9954 | nan | 0.7376 | 0.9908 |
| 0.007 | 130.0 | 1300 | 0.0533 | 0.8637 | 0.9207 | 0.9911 | nan | 0.8459 | 0.9955 | nan | 0.7366 | 0.9908 |
| 0.0063 | 132.0 | 1320 | 0.0526 | 0.8644 | 0.9259 | 0.9910 | nan | 0.8567 | 0.9951 | nan | 0.7380 | 0.9908 |
| 0.0101 | 134.0 | 1340 | 0.0462 | 0.8653 | 0.9067 | 0.9915 | nan | 0.8166 | 0.9968 | nan | 0.7393 | 0.9913 |
| 0.0183 | 136.0 | 1360 | 0.0516 | 0.5675 | 0.9024 | 0.9904 | nan | 0.8089 | 0.9959 | 0.0 | 0.7123 | 0.9901 |
| 0.0102 | 138.0 | 1380 | 0.0388 | 0.8366 | 0.8716 | 0.9898 | nan | 0.7460 | 0.9972 | nan | 0.6837 | 0.9896 |
| 0.0277 | 140.0 | 1400 | 0.0649 | 0.8159 | 0.9590 | 0.9850 | nan | 0.9313 | 0.9866 | nan | 0.6472 | 0.9846 |
| 0.0169 | 142.0 | 1420 | 0.0340 | 0.8444 | 0.9148 | 0.9894 | nan | 0.8355 | 0.9941 | nan | 0.6996 | 0.9891 |
| 0.0359 | 144.0 | 1440 | 0.0314 | 0.8667 | 0.8987 | 0.9918 | nan | 0.7997 | 0.9976 | nan | 0.7419 | 0.9916 |
| 0.0117 | 146.0 | 1460 | 0.0307 | 0.8517 | 0.8869 | 0.9908 | nan | 0.7765 | 0.9973 | nan | 0.7129 | 0.9905 |
| 0.0097 | 148.0 | 1480 | 0.0323 | 0.8757 | 0.9070 | 0.9924 | nan | 0.8162 | 0.9977 | nan | 0.7593 | 0.9922 |
| 0.0063 | 150.0 | 1500 | 0.0302 | 0.8808 | 0.9155 | 0.9926 | nan | 0.8335 | 0.9975 | nan | 0.7692 | 0.9924 |
| 0.0085 | 152.0 | 1520 | 0.0352 | 0.8697 | 0.9113 | 0.9918 | nan | 0.8258 | 0.9968 | nan | 0.7478 | 0.9916 |
| 0.0078 | 154.0 | 1540 | 0.0428 | 0.8649 | 0.9190 | 0.9912 | nan | 0.8423 | 0.9957 | nan | 0.7388 | 0.9910 |
| 0.0056 | 156.0 | 1560 | 0.0340 | 0.8709 | 0.9170 | 0.9918 | nan | 0.8376 | 0.9965 | nan | 0.7502 | 0.9916 |
| 0.0063 | 158.0 | 1580 | 0.0359 | 0.8661 | 0.9201 | 0.9913 | nan | 0.8445 | 0.9958 | nan | 0.7412 | 0.9911 |
| 0.0083 | 160.0 | 1600 | 0.0375 | 0.8684 | 0.9186 | 0.9915 | nan | 0.8410 | 0.9961 | nan | 0.7456 | 0.9913 |
| 0.0065 | 162.0 | 1620 | 0.0370 | 0.8699 | 0.9210 | 0.9916 | nan | 0.8459 | 0.9960 | nan | 0.7484 | 0.9914 |
| 0.0063 | 164.0 | 1640 | 0.0388 | 0.8699 | 0.9228 | 0.9916 | nan | 0.8498 | 0.9959 | nan | 0.7484 | 0.9913 |
| 0.0056 | 166.0 | 1660 | 0.0386 | 0.8702 | 0.9238 | 0.9916 | nan | 0.8517 | 0.9958 | nan | 0.7491 | 0.9914 |
| 0.0049 | 168.0 | 1680 | 0.0394 | 0.8703 | 0.9199 | 0.9917 | nan | 0.8436 | 0.9962 | nan | 0.7491 | 0.9914 |
| 0.0054 | 170.0 | 1700 | 0.0400 | 0.8704 | 0.9195 | 0.9917 | nan | 0.8428 | 0.9962 | nan | 0.7494 | 0.9915 |
| 0.0046 | 172.0 | 1720 | 0.0398 | 0.8728 | 0.9187 | 0.9919 | nan | 0.8410 | 0.9965 | nan | 0.7539 | 0.9917 |
| 0.0058 | 174.0 | 1740 | 0.0402 | 0.8711 | 0.9166 | 0.9918 | nan | 0.8367 | 0.9965 | nan | 0.7507 | 0.9916 |
| 0.005 | 176.0 | 1760 | 0.0400 | 0.8720 | 0.9196 | 0.9918 | nan | 0.8428 | 0.9963 | nan | 0.7525 | 0.9916 |
| 0.0061 | 178.0 | 1780 | 0.0417 | 0.8714 | 0.9226 | 0.9917 | nan | 0.8492 | 0.9960 | nan | 0.7513 | 0.9915 |
| 0.0061 | 180.0 | 1800 | 0.0407 | 0.8731 | 0.9249 | 0.9918 | nan | 0.8538 | 0.9960 | nan | 0.7545 | 0.9916 |
| 0.0065 | 182.0 | 1820 | 0.0420 | 0.8712 | 0.9235 | 0.9917 | nan | 0.8511 | 0.9959 | nan | 0.7509 | 0.9914 |
| 0.0045 | 184.0 | 1840 | 0.0421 | 0.8718 | 0.9250 | 0.9917 | nan | 0.8541 | 0.9959 | nan | 0.7522 | 0.9915 |
| 0.0056 | 186.0 | 1860 | 0.0435 | 0.8703 | 0.9157 | 0.9917 | nan | 0.8349 | 0.9965 | nan | 0.7490 | 0.9915 |
| 0.0059 | 188.0 | 1880 | 0.0436 | 0.8707 | 0.9191 | 0.9917 | nan | 0.8419 | 0.9963 | nan | 0.7499 | 0.9915 |
| 0.0042 | 190.0 | 1900 | 0.0436 | 0.8707 | 0.9210 | 0.9917 | nan | 0.8458 | 0.9961 | nan | 0.7499 | 0.9915 |
| 0.006 | 192.0 | 1920 | 0.0426 | 0.8697 | 0.9193 | 0.9916 | nan | 0.8425 | 0.9962 | nan | 0.7480 | 0.9914 |
| 0.0053 | 194.0 | 1940 | 0.0447 | 0.8697 | 0.9199 | 0.9916 | nan | 0.8437 | 0.9961 | nan | 0.7480 | 0.9914 |
| 0.0044 | 196.0 | 1960 | 0.0441 | 0.8710 | 0.9238 | 0.9916 | nan | 0.8516 | 0.9959 | nan | 0.7505 | 0.9914 |
| 0.0049 | 198.0 | 1980 | 0.0453 | 0.8693 | 0.9219 | 0.9915 | nan | 0.8479 | 0.9959 | nan | 0.7473 | 0.9913 |
| 0.0059 | 200.0 | 2000 | 0.0444 | 0.8726 | 0.9233 | 0.9918 | nan | 0.8506 | 0.9961 | nan | 0.7537 | 0.9916 |
| 0.005 | 202.0 | 2020 | 0.0447 | 0.8717 | 0.9256 | 0.9917 | nan | 0.8555 | 0.9958 | nan | 0.7519 | 0.9914 |
| 0.005 | 204.0 | 2040 | 0.0451 | 0.8711 | 0.9227 | 0.9917 | nan | 0.8494 | 0.9960 | nan | 0.7507 | 0.9915 |
| 0.0043 | 206.0 | 2060 | 0.0458 | 0.8707 | 0.9220 | 0.9916 | nan | 0.8480 | 0.9960 | nan | 0.7499 | 0.9914 |
| 0.0043 | 208.0 | 2080 | 0.0462 | 0.8696 | 0.9221 | 0.9916 | nan | 0.8482 | 0.9959 | nan | 0.7479 | 0.9913 |
| 0.0062 | 210.0 | 2100 | 0.0451 | 0.8715 | 0.9211 | 0.9917 | nan | 0.8461 | 0.9962 | nan | 0.7515 | 0.9915 |
| 0.0056 | 212.0 | 2120 | 0.0470 | 0.8706 | 0.9213 | 0.9917 | nan | 0.8466 | 0.9961 | nan | 0.7498 | 0.9914 |
| 0.0049 | 214.0 | 2140 | 0.0480 | 0.8679 | 0.9229 | 0.9914 | nan | 0.8500 | 0.9957 | nan | 0.7447 | 0.9912 |
| 0.0038 | 216.0 | 2160 | 0.0474 | 0.8700 | 0.9194 | 0.9916 | nan | 0.8427 | 0.9962 | nan | 0.7486 | 0.9914 |
| 0.0043 | 218.0 | 2180 | 0.0472 | 0.8693 | 0.9231 | 0.9915 | nan | 0.8503 | 0.9958 | nan | 0.7474 | 0.9913 |
| 0.005 | 220.0 | 2200 | 0.0471 | 0.8695 | 0.9156 | 0.9917 | nan | 0.8348 | 0.9964 | nan | 0.7475 | 0.9915 |
| 0.0041 | 222.0 | 2220 | 0.0472 | 0.8719 | 0.9187 | 0.9918 | nan | 0.8411 | 0.9964 | nan | 0.7522 | 0.9916 |
| 0.0041 | 224.0 | 2240 | 0.0471 | 0.8720 | 0.9219 | 0.9918 | nan | 0.8477 | 0.9962 | nan | 0.7525 | 0.9916 |
| 0.005 | 226.0 | 2260 | 0.0479 | 0.8720 | 0.9191 | 0.9918 | nan | 0.8418 | 0.9964 | nan | 0.7524 | 0.9916 |
| 0.0041 | 228.0 | 2280 | 0.0489 | 0.8706 | 0.9183 | 0.9917 | nan | 0.8403 | 0.9963 | nan | 0.7498 | 0.9915 |
| 0.005 | 230.0 | 2300 | 0.0472 | 0.8728 | 0.9195 | 0.9919 | nan | 0.8426 | 0.9964 | nan | 0.7540 | 0.9917 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|