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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: swin-tiny-patch4-window7-224-finetuned-woody_130epochs |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8921212121212121 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# swin-tiny-patch4-window7-224-finetuned-woody_130epochs |
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4550 |
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- Accuracy: 0.8921 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 130 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6694 | 1.0 | 58 | 0.6370 | 0.6594 | |
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| 0.6072 | 2.0 | 116 | 0.5813 | 0.7030 | |
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| 0.6048 | 3.0 | 174 | 0.5646 | 0.7030 | |
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| 0.5849 | 4.0 | 232 | 0.5778 | 0.6970 | |
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| 0.5671 | 5.0 | 290 | 0.5394 | 0.7236 | |
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| 0.5575 | 6.0 | 348 | 0.5212 | 0.7382 | |
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| 0.568 | 7.0 | 406 | 0.5218 | 0.7358 | |
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| 0.5607 | 8.0 | 464 | 0.5183 | 0.7527 | |
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| 0.5351 | 9.0 | 522 | 0.5138 | 0.7467 | |
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| 0.5459 | 10.0 | 580 | 0.5290 | 0.7394 | |
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| 0.5454 | 11.0 | 638 | 0.5212 | 0.7345 | |
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| 0.5291 | 12.0 | 696 | 0.5130 | 0.7576 | |
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| 0.5378 | 13.0 | 754 | 0.5372 | 0.7503 | |
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| 0.5264 | 14.0 | 812 | 0.6089 | 0.6861 | |
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| 0.4909 | 15.0 | 870 | 0.4852 | 0.7636 | |
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| 0.5591 | 16.0 | 928 | 0.4817 | 0.76 | |
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| 0.4966 | 17.0 | 986 | 0.5673 | 0.6933 | |
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| 0.4988 | 18.0 | 1044 | 0.5131 | 0.7418 | |
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| 0.5339 | 19.0 | 1102 | 0.4998 | 0.7394 | |
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| 0.4804 | 20.0 | 1160 | 0.4655 | 0.7733 | |
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| 0.503 | 21.0 | 1218 | 0.4554 | 0.7685 | |
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| 0.4859 | 22.0 | 1276 | 0.4713 | 0.7770 | |
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| 0.504 | 23.0 | 1334 | 0.4545 | 0.7721 | |
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| 0.478 | 24.0 | 1392 | 0.4658 | 0.7830 | |
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| 0.4759 | 25.0 | 1450 | 0.4365 | 0.8012 | |
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| 0.4686 | 26.0 | 1508 | 0.4452 | 0.7855 | |
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| 0.4668 | 27.0 | 1566 | 0.4427 | 0.7879 | |
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| 0.4615 | 28.0 | 1624 | 0.4439 | 0.7685 | |
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| 0.4588 | 29.0 | 1682 | 0.4378 | 0.7830 | |
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| 0.4588 | 30.0 | 1740 | 0.4229 | 0.7988 | |
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| 0.4296 | 31.0 | 1798 | 0.4188 | 0.7976 | |
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| 0.4208 | 32.0 | 1856 | 0.4316 | 0.7891 | |
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| 0.4481 | 33.0 | 1914 | 0.4331 | 0.7891 | |
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| 0.4253 | 34.0 | 1972 | 0.4524 | 0.7879 | |
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| 0.4117 | 35.0 | 2030 | 0.4570 | 0.7952 | |
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| 0.4405 | 36.0 | 2088 | 0.4307 | 0.7927 | |
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| 0.4154 | 37.0 | 2146 | 0.4257 | 0.8024 | |
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| 0.3962 | 38.0 | 2204 | 0.5077 | 0.7818 | |
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| 0.414 | 39.0 | 2262 | 0.4602 | 0.8012 | |
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| 0.3937 | 40.0 | 2320 | 0.4741 | 0.7770 | |
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| 0.4186 | 41.0 | 2378 | 0.4250 | 0.8 | |
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| 0.4076 | 42.0 | 2436 | 0.4353 | 0.7988 | |
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| 0.3777 | 43.0 | 2494 | 0.4442 | 0.7879 | |
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| 0.3968 | 44.0 | 2552 | 0.4525 | 0.7879 | |
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| 0.377 | 45.0 | 2610 | 0.4198 | 0.7988 | |
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| 0.378 | 46.0 | 2668 | 0.4297 | 0.8097 | |
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| 0.3675 | 47.0 | 2726 | 0.4435 | 0.8085 | |
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| 0.3562 | 48.0 | 2784 | 0.4477 | 0.7952 | |
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| 0.381 | 49.0 | 2842 | 0.4206 | 0.8255 | |
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| 0.3603 | 50.0 | 2900 | 0.4136 | 0.8109 | |
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| 0.3331 | 51.0 | 2958 | 0.4141 | 0.8230 | |
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| 0.3471 | 52.0 | 3016 | 0.4253 | 0.8109 | |
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| 0.346 | 53.0 | 3074 | 0.5203 | 0.8048 | |
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| 0.3481 | 54.0 | 3132 | 0.4288 | 0.8242 | |
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| 0.3411 | 55.0 | 3190 | 0.4416 | 0.8194 | |
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| 0.3275 | 56.0 | 3248 | 0.4149 | 0.8291 | |
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| 0.3067 | 57.0 | 3306 | 0.4623 | 0.8218 | |
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| 0.3166 | 58.0 | 3364 | 0.4432 | 0.8255 | |
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| 0.3294 | 59.0 | 3422 | 0.4599 | 0.8267 | |
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| 0.3146 | 60.0 | 3480 | 0.4266 | 0.8291 | |
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| 0.3091 | 61.0 | 3538 | 0.4318 | 0.8315 | |
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| 0.3277 | 62.0 | 3596 | 0.4252 | 0.8242 | |
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| 0.296 | 63.0 | 3654 | 0.4332 | 0.8436 | |
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| 0.3241 | 64.0 | 3712 | 0.4729 | 0.8194 | |
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| 0.3104 | 65.0 | 3770 | 0.4228 | 0.8448 | |
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| 0.2878 | 66.0 | 3828 | 0.4173 | 0.8388 | |
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| 0.265 | 67.0 | 3886 | 0.4210 | 0.8497 | |
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| 0.3011 | 68.0 | 3944 | 0.4276 | 0.8436 | |
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| 0.2861 | 69.0 | 4002 | 0.4923 | 0.8315 | |
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| 0.2994 | 70.0 | 4060 | 0.4472 | 0.8182 | |
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| 0.276 | 71.0 | 4118 | 0.4541 | 0.8315 | |
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| 0.2796 | 72.0 | 4176 | 0.4218 | 0.8521 | |
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| 0.2727 | 73.0 | 4234 | 0.4053 | 0.8448 | |
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| 0.255 | 74.0 | 4292 | 0.4356 | 0.8376 | |
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| 0.276 | 75.0 | 4350 | 0.4193 | 0.8436 | |
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| 0.261 | 76.0 | 4408 | 0.4484 | 0.8533 | |
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| 0.2416 | 77.0 | 4466 | 0.4722 | 0.8194 | |
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| 0.2602 | 78.0 | 4524 | 0.4431 | 0.8533 | |
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| 0.2591 | 79.0 | 4582 | 0.4269 | 0.8606 | |
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| 0.2613 | 80.0 | 4640 | 0.4335 | 0.8485 | |
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| 0.2555 | 81.0 | 4698 | 0.4269 | 0.8594 | |
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| 0.2832 | 82.0 | 4756 | 0.3968 | 0.8715 | |
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| 0.264 | 83.0 | 4814 | 0.4173 | 0.8703 | |
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| 0.2462 | 84.0 | 4872 | 0.4150 | 0.8606 | |
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| 0.2424 | 85.0 | 4930 | 0.4377 | 0.8630 | |
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| 0.2574 | 86.0 | 4988 | 0.4120 | 0.8679 | |
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| 0.2273 | 87.0 | 5046 | 0.4393 | 0.8533 | |
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| 0.2334 | 88.0 | 5104 | 0.4366 | 0.8630 | |
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| 0.2258 | 89.0 | 5162 | 0.4189 | 0.8630 | |
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| 0.2153 | 90.0 | 5220 | 0.4474 | 0.8630 | |
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| 0.2462 | 91.0 | 5278 | 0.4362 | 0.8642 | |
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| 0.2356 | 92.0 | 5336 | 0.4454 | 0.8715 | |
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| 0.2019 | 93.0 | 5394 | 0.4413 | 0.88 | |
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| 0.209 | 94.0 | 5452 | 0.4410 | 0.8703 | |
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| 0.2201 | 95.0 | 5510 | 0.4323 | 0.8691 | |
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| 0.2245 | 96.0 | 5568 | 0.4999 | 0.8618 | |
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| 0.2178 | 97.0 | 5626 | 0.4612 | 0.8655 | |
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| 0.2163 | 98.0 | 5684 | 0.4340 | 0.8703 | |
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| 0.2228 | 99.0 | 5742 | 0.4504 | 0.8788 | |
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| 0.2151 | 100.0 | 5800 | 0.4602 | 0.8703 | |
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| 0.1988 | 101.0 | 5858 | 0.4414 | 0.8812 | |
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| 0.2227 | 102.0 | 5916 | 0.4392 | 0.8824 | |
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| 0.1772 | 103.0 | 5974 | 0.5069 | 0.8630 | |
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| 0.2199 | 104.0 | 6032 | 0.4648 | 0.8667 | |
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| 0.1936 | 105.0 | 6090 | 0.4806 | 0.8691 | |
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| 0.199 | 106.0 | 6148 | 0.4569 | 0.8764 | |
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| 0.2149 | 107.0 | 6206 | 0.4445 | 0.8739 | |
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| 0.1917 | 108.0 | 6264 | 0.4444 | 0.8727 | |
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| 0.201 | 109.0 | 6322 | 0.4594 | 0.8727 | |
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| 0.1938 | 110.0 | 6380 | 0.4564 | 0.8764 | |
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| 0.1977 | 111.0 | 6438 | 0.4398 | 0.8739 | |
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| 0.1776 | 112.0 | 6496 | 0.4356 | 0.88 | |
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| 0.1939 | 113.0 | 6554 | 0.4412 | 0.8848 | |
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| 0.178 | 114.0 | 6612 | 0.4373 | 0.88 | |
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| 0.1926 | 115.0 | 6670 | 0.4508 | 0.8812 | |
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| 0.1979 | 116.0 | 6728 | 0.4477 | 0.8848 | |
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| 0.1958 | 117.0 | 6786 | 0.4488 | 0.8897 | |
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| 0.189 | 118.0 | 6844 | 0.4553 | 0.8836 | |
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| 0.1838 | 119.0 | 6902 | 0.4605 | 0.8848 | |
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| 0.1755 | 120.0 | 6960 | 0.4463 | 0.8836 | |
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| 0.1958 | 121.0 | 7018 | 0.4474 | 0.8861 | |
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| 0.1857 | 122.0 | 7076 | 0.4550 | 0.8921 | |
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| 0.1466 | 123.0 | 7134 | 0.4494 | 0.8885 | |
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| 0.1751 | 124.0 | 7192 | 0.4560 | 0.8873 | |
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| 0.175 | 125.0 | 7250 | 0.4383 | 0.8897 | |
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| 0.207 | 126.0 | 7308 | 0.4601 | 0.8873 | |
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| 0.1756 | 127.0 | 7366 | 0.4425 | 0.8897 | |
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| 0.1695 | 128.0 | 7424 | 0.4533 | 0.8909 | |
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| 0.1873 | 129.0 | 7482 | 0.4510 | 0.8897 | |
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| 0.1726 | 130.0 | 7540 | 0.4463 | 0.8909 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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