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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/swin-tiny-patch4-window7-224
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+ tags:
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+ - image-classification
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+ - vision
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: swin-tiny
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+ results: []
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+ ---
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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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+
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+ # swin-tiny
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+
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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 cifar100 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5505
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+ - Accuracy: 0.8646
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 256
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+ - seed: 42
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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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+ - num_epochs: 300
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 2.8188 | 1.0 | 333 | 2.4232 | 0.4372 |
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+ | 2.0411 | 2.0 | 666 | 1.4235 | 0.6269 |
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+ | 1.7069 | 3.0 | 999 | 1.0558 | 0.7102 |
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+ | 1.5722 | 4.0 | 1332 | 0.8657 | 0.7504 |
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+ | 1.346 | 5.0 | 1665 | 0.7774 | 0.7721 |
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+ | 1.303 | 6.0 | 1998 | 0.7138 | 0.7874 |
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+ | 1.2045 | 7.0 | 2331 | 0.6616 | 0.7986 |
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+ | 1.2482 | 8.0 | 2664 | 0.6210 | 0.8128 |
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+ | 1.1202 | 9.0 | 2997 | 0.5925 | 0.8185 |
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+ | 1.0021 | 10.0 | 3330 | 0.5728 | 0.8235 |
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+ | 1.0662 | 11.0 | 3663 | 0.5637 | 0.829 |
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+ | 1.0263 | 12.0 | 3996 | 0.5442 | 0.8303 |
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+ | 1.0581 | 13.0 | 4329 | 0.5319 | 0.8379 |
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+ | 0.9922 | 14.0 | 4662 | 0.5215 | 0.8388 |
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+ | 0.9643 | 15.0 | 4995 | 0.5144 | 0.8399 |
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+ | 0.9687 | 16.0 | 5328 | 0.5103 | 0.8413 |
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+ | 0.9464 | 17.0 | 5661 | 0.5021 | 0.8422 |
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+ | 0.8651 | 18.0 | 5994 | 0.4867 | 0.8483 |
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+ | 0.8122 | 19.0 | 6327 | 0.4865 | 0.8457 |
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+ | 0.7918 | 20.0 | 6660 | 0.4877 | 0.8486 |
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+ | 0.8994 | 21.0 | 6993 | 0.4836 | 0.8502 |
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+ | 0.8661 | 22.0 | 7326 | 0.4736 | 0.8538 |
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+ | 0.869 | 23.0 | 7659 | 0.4703 | 0.8528 |
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+ | 0.8681 | 24.0 | 7992 | 0.4798 | 0.8513 |
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+ | 0.7693 | 25.0 | 8325 | 0.4680 | 0.8523 |
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+ | 0.8693 | 26.0 | 8658 | 0.4646 | 0.8579 |
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+ | 0.8041 | 27.0 | 8991 | 0.4686 | 0.8555 |
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+ | 0.8036 | 28.0 | 9324 | 0.4609 | 0.8578 |
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+ | 0.7571 | 29.0 | 9657 | 0.4597 | 0.8616 |
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+ | 0.7666 | 30.0 | 9990 | 0.4581 | 0.8606 |
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+ | 0.7226 | 31.0 | 10323 | 0.4569 | 0.8601 |
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+ | 0.7179 | 32.0 | 10656 | 0.4573 | 0.8628 |
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+ | 0.6866 | 33.0 | 10989 | 0.4567 | 0.8606 |
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+ | 0.7002 | 34.0 | 11322 | 0.4672 | 0.8576 |
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+ | 0.7499 | 35.0 | 11655 | 0.4624 | 0.8611 |
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+ | 0.7393 | 36.0 | 11988 | 0.4579 | 0.8604 |
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+ | 0.7393 | 37.0 | 12321 | 0.4560 | 0.8619 |
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+ | 0.7599 | 38.0 | 12654 | 0.4503 | 0.8637 |
92
+ | 0.6636 | 39.0 | 12987 | 0.4542 | 0.8636 |
93
+ | 0.6759 | 40.0 | 13320 | 0.4483 | 0.8631 |
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+ | 0.7266 | 41.0 | 13653 | 0.4484 | 0.8636 |
95
+ | 0.6819 | 42.0 | 13986 | 0.4453 | 0.8647 |
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+ | 0.5912 | 43.0 | 14319 | 0.4493 | 0.864 |
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+ | 0.6803 | 44.0 | 14652 | 0.4453 | 0.8646 |
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+ | 0.6898 | 45.0 | 14985 | 0.4458 | 0.8628 |
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+ | 0.6312 | 46.0 | 15318 | 0.4499 | 0.8636 |
100
+ | 0.6972 | 47.0 | 15651 | 0.4494 | 0.8646 |
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+ | 0.616 | 48.0 | 15984 | 0.4525 | 0.8674 |
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+ | 0.6911 | 49.0 | 16317 | 0.4506 | 0.8637 |
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+ | 0.6737 | 50.0 | 16650 | 0.4504 | 0.8648 |
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+ | 0.5573 | 51.0 | 16983 | 0.4542 | 0.8641 |
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+ | 0.6296 | 52.0 | 17316 | 0.4573 | 0.8626 |
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+ | 0.6245 | 53.0 | 17649 | 0.4550 | 0.8647 |
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+ | 0.6018 | 54.0 | 17982 | 0.4509 | 0.8668 |
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+ | 0.6068 | 55.0 | 18315 | 0.4561 | 0.865 |
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+ | 0.6368 | 56.0 | 18648 | 0.4533 | 0.8666 |
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+ | 0.5945 | 57.0 | 18981 | 0.4537 | 0.8646 |
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+ | 0.5379 | 58.0 | 19314 | 0.4583 | 0.8644 |
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+ | 0.6031 | 59.0 | 19647 | 0.4574 | 0.8647 |
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+ | 0.5445 | 60.0 | 19980 | 0.4607 | 0.8629 |
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+ | 0.5589 | 61.0 | 20313 | 0.4619 | 0.8649 |
115
+ | 0.5777 | 62.0 | 20646 | 0.4740 | 0.8626 |
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+ | 0.5711 | 63.0 | 20979 | 0.4684 | 0.8659 |
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+ | 0.5369 | 64.0 | 21312 | 0.4655 | 0.8639 |
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+ | 0.5454 | 65.0 | 21645 | 0.4574 | 0.867 |
119
+ | 0.5471 | 66.0 | 21978 | 0.4579 | 0.8655 |
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+ | 0.5816 | 67.0 | 22311 | 0.4610 | 0.8662 |
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+ | 0.5262 | 68.0 | 22644 | 0.4631 | 0.8646 |
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+ | 0.5163 | 69.0 | 22977 | 0.4532 | 0.8677 |
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+ | 0.5231 | 70.0 | 23310 | 0.4635 | 0.867 |
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+ | 0.5672 | 71.0 | 23643 | 0.4626 | 0.8668 |
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+ | 0.501 | 72.0 | 23976 | 0.4601 | 0.8677 |
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+ | 0.527 | 73.0 | 24309 | 0.4661 | 0.8644 |
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+ | 0.5618 | 74.0 | 24642 | 0.4677 | 0.8664 |
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+ | 0.5161 | 75.0 | 24975 | 0.4630 | 0.8691 |
129
+ | 0.5158 | 76.0 | 25308 | 0.4691 | 0.8671 |
130
+ | 0.54 | 77.0 | 25641 | 0.4645 | 0.8696 |
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+ | 0.5352 | 78.0 | 25974 | 0.4805 | 0.8649 |
132
+ | 0.5433 | 79.0 | 26307 | 0.4696 | 0.867 |
133
+ | 0.5555 | 80.0 | 26640 | 0.4745 | 0.8657 |
134
+ | 0.5248 | 81.0 | 26973 | 0.4767 | 0.8655 |
135
+ | 0.4648 | 82.0 | 27306 | 0.4730 | 0.8681 |
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+ | 0.5853 | 83.0 | 27639 | 0.4781 | 0.8656 |
137
+ | 0.5298 | 84.0 | 27972 | 0.4729 | 0.869 |
138
+ | 0.4484 | 85.0 | 28305 | 0.4741 | 0.869 |
139
+ | 0.4765 | 86.0 | 28638 | 0.4877 | 0.8633 |
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+ | 0.4778 | 88.0 | 29304 | 0.4753 | 0.8677 |
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+ | 0.508 | 89.0 | 29637 | 0.4750 | 0.867 |
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+ | 0.4567 | 90.0 | 29970 | 0.4816 | 0.8681 |
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+ | 0.4828 | 91.0 | 30303 | 0.4806 | 0.8659 |
145
+ | 0.4357 | 92.0 | 30636 | 0.4770 | 0.8676 |
146
+ | 0.5117 | 93.0 | 30969 | 0.4741 | 0.8714 |
147
+ | 0.4756 | 94.0 | 31302 | 0.4860 | 0.8639 |
148
+ | 0.4575 | 95.0 | 31635 | 0.4855 | 0.8652 |
149
+ | 0.4657 | 96.0 | 31968 | 0.4828 | 0.8677 |
150
+ | 0.4746 | 97.0 | 32301 | 0.4850 | 0.8676 |
151
+ | 0.5466 | 98.0 | 32634 | 0.4890 | 0.8662 |
152
+ | 0.49 | 99.0 | 32967 | 0.4879 | 0.8663 |
153
+ | 0.4886 | 100.0 | 33300 | 0.4859 | 0.869 |
154
+ | 0.4763 | 101.0 | 33633 | 0.4840 | 0.868 |
155
+ | 0.5143 | 102.0 | 33966 | 0.4940 | 0.8673 |
156
+ | 0.4732 | 103.0 | 34299 | 0.4827 | 0.8699 |
157
+ | 0.481 | 104.0 | 34632 | 0.4891 | 0.8686 |
158
+ | 0.5015 | 105.0 | 34965 | 0.5004 | 0.8651 |
159
+ | 0.4596 | 106.0 | 35298 | 0.4950 | 0.8669 |
160
+ | 0.4201 | 107.0 | 35631 | 0.4920 | 0.866 |
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+ | 0.4358 | 108.0 | 35964 | 0.4954 | 0.8643 |
162
+ | 0.4588 | 109.0 | 36297 | 0.4923 | 0.8649 |
163
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164
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165
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166
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167
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168
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169
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170
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172
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173
+ | 0.4095 | 120.0 | 39960 | 0.4992 | 0.8674 |
174
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175
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176
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177
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178
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179
+ | 0.4485 | 126.0 | 41958 | 0.5067 | 0.8636 |
180
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182
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183
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184
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185
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186
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187
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188
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189
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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206
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207
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208
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209
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210
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211
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212
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213
+ | 0.355 | 160.0 | 53280 | 0.5265 | 0.8626 |
214
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215
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216
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217
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218
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219
+ | 0.3415 | 166.0 | 55278 | 0.5249 | 0.8654 |
220
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221
+ | 0.3705 | 168.0 | 55944 | 0.5301 | 0.8645 |
222
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223
+ | 0.3473 | 170.0 | 56610 | 0.5298 | 0.8646 |
224
+ | 0.3825 | 171.0 | 56943 | 0.5256 | 0.8643 |
225
+ | 0.3841 | 172.0 | 57276 | 0.5229 | 0.8668 |
226
+ | 0.3543 | 173.0 | 57609 | 0.5270 | 0.8646 |
227
+ | 0.4086 | 174.0 | 57942 | 0.5240 | 0.8656 |
228
+ | 0.3832 | 175.0 | 58275 | 0.5280 | 0.8631 |
229
+ | 0.3515 | 176.0 | 58608 | 0.5302 | 0.8645 |
230
+ | 0.3749 | 177.0 | 58941 | 0.5316 | 0.8645 |
231
+ | 0.3298 | 178.0 | 59274 | 0.5290 | 0.8647 |
232
+ | 0.3758 | 179.0 | 59607 | 0.5272 | 0.8668 |
233
+ | 0.31 | 180.0 | 59940 | 0.5314 | 0.864 |
234
+ | 0.3521 | 181.0 | 60273 | 0.5259 | 0.8648 |
235
+ | 0.3922 | 182.0 | 60606 | 0.5316 | 0.8638 |
236
+ | 0.3391 | 183.0 | 60939 | 0.5316 | 0.8648 |
237
+ | 0.3646 | 184.0 | 61272 | 0.5329 | 0.8637 |
238
+ | 0.4033 | 185.0 | 61605 | 0.5357 | 0.8662 |
239
+ | 0.395 | 186.0 | 61938 | 0.5376 | 0.8634 |
240
+ | 0.3253 | 187.0 | 62271 | 0.5346 | 0.8647 |
241
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242
+ | 0.3494 | 189.0 | 62937 | 0.5332 | 0.864 |
243
+ | 0.4009 | 190.0 | 63270 | 0.5364 | 0.8639 |
244
+ | 0.3935 | 191.0 | 63603 | 0.5329 | 0.8668 |
245
+ | 0.3666 | 192.0 | 63936 | 0.5337 | 0.8641 |
246
+ | 0.3474 | 193.0 | 64269 | 0.5321 | 0.866 |
247
+ | 0.3873 | 194.0 | 64602 | 0.5336 | 0.8635 |
248
+ | 0.3722 | 195.0 | 64935 | 0.5319 | 0.8645 |
249
+ | 0.3525 | 196.0 | 65268 | 0.5347 | 0.8636 |
250
+ | 0.3561 | 197.0 | 65601 | 0.5407 | 0.8629 |
251
+ | 0.3946 | 198.0 | 65934 | 0.5361 | 0.8643 |
252
+ | 0.3768 | 199.0 | 66267 | 0.5387 | 0.8639 |
253
+ | 0.3328 | 200.0 | 66600 | 0.5325 | 0.8656 |
254
+ | 0.3418 | 201.0 | 66933 | 0.5306 | 0.8676 |
255
+ | 0.3542 | 202.0 | 67266 | 0.5321 | 0.8648 |
256
+ | 0.3688 | 203.0 | 67599 | 0.5430 | 0.8598 |
257
+ | 0.3685 | 204.0 | 67932 | 0.5405 | 0.8629 |
258
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259
+ | 0.358 | 206.0 | 68598 | 0.5403 | 0.8621 |
260
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261
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262
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263
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264
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265
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266
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267
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268
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269
+ | 0.3724 | 216.0 | 71928 | 0.5359 | 0.8665 |
270
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271
+ | 0.3484 | 218.0 | 72594 | 0.5407 | 0.8638 |
272
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273
+ | 0.3703 | 220.0 | 73260 | 0.5471 | 0.8641 |
274
+ | 0.3318 | 221.0 | 73593 | 0.5432 | 0.8638 |
275
+ | 0.3573 | 222.0 | 73926 | 0.5473 | 0.8631 |
276
+ | 0.3308 | 223.0 | 74259 | 0.5448 | 0.8663 |
277
+ | 0.3329 | 224.0 | 74592 | 0.5445 | 0.8635 |
278
+ | 0.3429 | 225.0 | 74925 | 0.5445 | 0.8631 |
279
+ | 0.3494 | 226.0 | 75258 | 0.5433 | 0.8632 |
280
+ | 0.327 | 227.0 | 75591 | 0.5457 | 0.8639 |
281
+ | 0.313 | 228.0 | 75924 | 0.5457 | 0.8651 |
282
+ | 0.3344 | 229.0 | 76257 | 0.5421 | 0.8649 |
283
+ | 0.2893 | 230.0 | 76590 | 0.5472 | 0.8645 |
284
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285
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286
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287
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288
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289
+ | 0.292 | 236.0 | 78588 | 0.5419 | 0.8658 |
290
+ | 0.3863 | 237.0 | 78921 | 0.5445 | 0.8637 |
291
+ | 0.3368 | 238.0 | 79254 | 0.5451 | 0.8643 |
292
+ | 0.3011 | 239.0 | 79587 | 0.5459 | 0.8651 |
293
+ | 0.2977 | 240.0 | 79920 | 0.5476 | 0.8651 |
294
+ | 0.3695 | 241.0 | 80253 | 0.5412 | 0.8649 |
295
+ | 0.3683 | 242.0 | 80586 | 0.5449 | 0.865 |
296
+ | 0.2971 | 243.0 | 80919 | 0.5490 | 0.8658 |
297
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+
355
+
356
+ ### Framework versions
357
+
358
+ - Transformers 4.39.3
359
+ - Pytorch 2.2.2+cu118
360
+ - Datasets 2.18.0
361
+ - Tokenizers 0.15.2
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