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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-tiny-1k-224 |
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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: convnextv2-tiny-1k-224-finetuned-pattern-edge |
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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: 0.81 |
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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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# convnextv2-tiny-1k-224-finetuned-pattern-edge |
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This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9202 |
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- Accuracy: 0.81 |
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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: 120 |
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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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| 2.302 | 0.9912 | 28 | 2.2666 | 0.1575 | |
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| 2.2226 | 1.9823 | 56 | 2.1654 | 0.315 | |
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| 2.0639 | 2.9735 | 84 | 1.9970 | 0.445 | |
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| 1.8559 | 4.0 | 113 | 1.7373 | 0.56 | |
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| 1.5966 | 4.9912 | 141 | 1.4823 | 0.605 | |
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| 1.3967 | 5.9823 | 169 | 1.2925 | 0.6125 | |
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| 1.204 | 6.9735 | 197 | 1.0512 | 0.68 | |
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| 1.0206 | 8.0 | 226 | 0.9307 | 0.7025 | |
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| 0.9408 | 8.9912 | 254 | 0.8286 | 0.7425 | |
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| 0.8501 | 9.9823 | 282 | 0.8590 | 0.6975 | |
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| 0.7545 | 10.9735 | 310 | 0.7702 | 0.7475 | |
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| 0.7484 | 12.0 | 339 | 0.7739 | 0.765 | |
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| 0.6909 | 12.9912 | 367 | 0.7344 | 0.75 | |
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| 0.6558 | 13.9823 | 395 | 0.6874 | 0.775 | |
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| 0.5923 | 14.9735 | 423 | 0.6641 | 0.7675 | |
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| 0.5764 | 16.0 | 452 | 0.6110 | 0.7925 | |
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| 0.5235 | 16.9912 | 480 | 0.6806 | 0.76 | |
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| 0.4883 | 17.9823 | 508 | 0.7903 | 0.76 | |
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| 0.4682 | 18.9735 | 536 | 0.6469 | 0.7825 | |
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| 0.441 | 20.0 | 565 | 0.6694 | 0.7825 | |
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| 0.4201 | 20.9912 | 593 | 0.7145 | 0.7625 | |
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| 0.387 | 21.9823 | 621 | 0.6505 | 0.7775 | |
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| 0.4034 | 22.9735 | 649 | 0.6169 | 0.7875 | |
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| 0.3041 | 24.0 | 678 | 0.6416 | 0.795 | |
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| 0.3021 | 24.9912 | 706 | 0.6992 | 0.775 | |
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| 0.2853 | 25.9823 | 734 | 0.6566 | 0.7975 | |
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| 0.27 | 26.9735 | 762 | 0.6970 | 0.7825 | |
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| 0.2722 | 28.0 | 791 | 0.6863 | 0.785 | |
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| 0.2143 | 28.9912 | 819 | 0.6794 | 0.795 | |
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| 0.2238 | 29.9823 | 847 | 0.6782 | 0.7975 | |
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| 0.2387 | 30.9735 | 875 | 0.6945 | 0.81 | |
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| 0.223 | 32.0 | 904 | 0.7377 | 0.7825 | |
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| 0.2211 | 32.9912 | 932 | 0.7431 | 0.7775 | |
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| 0.1882 | 33.9823 | 960 | 0.7029 | 0.815 | |
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| 0.1562 | 34.9735 | 988 | 0.6887 | 0.815 | |
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| 0.1689 | 36.0 | 1017 | 0.7190 | 0.7975 | |
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| 0.1886 | 36.9912 | 1045 | 0.7678 | 0.795 | |
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| 0.1887 | 37.9823 | 1073 | 0.7334 | 0.81 | |
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| 0.1531 | 38.9735 | 1101 | 0.7359 | 0.7925 | |
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| 0.1662 | 40.0 | 1130 | 0.7594 | 0.8075 | |
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| 0.1273 | 40.9912 | 1158 | 0.7342 | 0.81 | |
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| 0.1986 | 41.9823 | 1186 | 0.7781 | 0.805 | |
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| 0.1891 | 42.9735 | 1214 | 0.7376 | 0.8225 | |
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| 0.1573 | 44.0 | 1243 | 0.7304 | 0.815 | |
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| 0.1536 | 44.9912 | 1271 | 0.7773 | 0.8 | |
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| 0.1562 | 45.9823 | 1299 | 0.7623 | 0.8 | |
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| 0.1264 | 46.9735 | 1327 | 0.8314 | 0.7925 | |
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| 0.1596 | 48.0 | 1356 | 0.7831 | 0.8175 | |
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| 0.1237 | 48.9912 | 1384 | 0.7949 | 0.8 | |
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| 0.1355 | 49.9823 | 1412 | 0.7813 | 0.795 | |
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| 0.1251 | 50.9735 | 1440 | 0.7647 | 0.81 | |
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| 0.1181 | 52.0 | 1469 | 0.7552 | 0.8175 | |
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| 0.1224 | 52.9912 | 1497 | 0.8346 | 0.795 | |
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| 0.1201 | 53.9823 | 1525 | 0.7741 | 0.7975 | |
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| 0.1109 | 54.9735 | 1553 | 0.7724 | 0.785 | |
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| 0.1084 | 56.0 | 1582 | 0.7904 | 0.805 | |
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| 0.1187 | 56.9912 | 1610 | 0.7424 | 0.8125 | |
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| 0.0935 | 57.9823 | 1638 | 0.7411 | 0.815 | |
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| 0.1023 | 58.9735 | 1666 | 0.7476 | 0.81 | |
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| 0.1166 | 60.0 | 1695 | 0.7742 | 0.8175 | |
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| 0.099 | 60.9912 | 1723 | 0.7697 | 0.815 | |
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| 0.1157 | 61.9823 | 1751 | 0.8538 | 0.8 | |
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| 0.1137 | 62.9735 | 1779 | 0.8545 | 0.8125 | |
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| 0.094 | 64.0 | 1808 | 0.8463 | 0.7925 | |
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| 0.1161 | 64.9912 | 1836 | 0.8351 | 0.81 | |
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| 0.08 | 65.9823 | 1864 | 0.8610 | 0.7925 | |
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| 0.0799 | 66.9735 | 1892 | 0.8593 | 0.8075 | |
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| 0.0783 | 68.0 | 1921 | 0.8423 | 0.815 | |
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| 0.0851 | 68.9912 | 1949 | 0.8265 | 0.82 | |
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| 0.0775 | 69.9823 | 1977 | 0.8708 | 0.805 | |
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| 0.0902 | 70.9735 | 2005 | 0.8181 | 0.81 | |
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| 0.0904 | 72.0 | 2034 | 0.8297 | 0.82 | |
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| 0.0898 | 72.9912 | 2062 | 0.8464 | 0.82 | |
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| 0.1013 | 73.9823 | 2090 | 0.8325 | 0.81 | |
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| 0.0726 | 74.9735 | 2118 | 0.8772 | 0.8 | |
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| 0.0745 | 76.0 | 2147 | 0.8505 | 0.8125 | |
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| 0.0891 | 76.9912 | 2175 | 0.8694 | 0.81 | |
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| 0.0791 | 77.9823 | 2203 | 0.8766 | 0.81 | |
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| 0.0639 | 78.9735 | 2231 | 0.8462 | 0.8125 | |
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| 0.0676 | 80.0 | 2260 | 0.8991 | 0.8075 | |
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| 0.0904 | 80.9912 | 2288 | 0.8551 | 0.815 | |
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| 0.0788 | 81.9823 | 2316 | 0.9302 | 0.795 | |
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| 0.0787 | 82.9735 | 2344 | 0.8706 | 0.8025 | |
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| 0.0918 | 84.0 | 2373 | 0.8680 | 0.805 | |
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| 0.0681 | 84.9912 | 2401 | 0.8481 | 0.8125 | |
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| 0.115 | 85.9823 | 2429 | 0.8553 | 0.8025 | |
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| 0.0599 | 86.9735 | 2457 | 0.8887 | 0.805 | |
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| 0.0774 | 88.0 | 2486 | 0.9255 | 0.81 | |
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| 0.0701 | 88.9912 | 2514 | 0.8795 | 0.81 | |
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| 0.074 | 89.9823 | 2542 | 0.8634 | 0.8175 | |
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| 0.0497 | 90.9735 | 2570 | 0.8793 | 0.82 | |
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| 0.0569 | 92.0 | 2599 | 0.9007 | 0.7925 | |
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| 0.0722 | 92.9912 | 2627 | 0.8701 | 0.815 | |
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| 0.0674 | 93.9823 | 2655 | 0.8880 | 0.8225 | |
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| 0.0643 | 94.9735 | 2683 | 0.8855 | 0.8075 | |
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| 0.0583 | 96.0 | 2712 | 0.8918 | 0.815 | |
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| 0.0558 | 96.9912 | 2740 | 0.8736 | 0.8275 | |
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| 0.0622 | 97.9823 | 2768 | 0.9058 | 0.815 | |
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| 0.0689 | 98.9735 | 2796 | 0.9007 | 0.8075 | |
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| 0.0782 | 100.0 | 2825 | 0.9216 | 0.8025 | |
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| 0.0696 | 100.9912 | 2853 | 0.9159 | 0.8075 | |
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| 0.0554 | 101.9823 | 2881 | 0.9195 | 0.8125 | |
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| 0.0585 | 102.9735 | 2909 | 0.9314 | 0.8125 | |
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| 0.0541 | 104.0 | 2938 | 0.8939 | 0.825 | |
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| 0.0636 | 104.9912 | 2966 | 0.9045 | 0.8025 | |
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| 0.0684 | 105.9823 | 2994 | 0.8892 | 0.8075 | |
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| 0.0608 | 106.9735 | 3022 | 0.8999 | 0.8075 | |
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| 0.0663 | 108.0 | 3051 | 0.9033 | 0.8075 | |
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| 0.054 | 108.9912 | 3079 | 0.9249 | 0.805 | |
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| 0.0538 | 109.9823 | 3107 | 0.9065 | 0.81 | |
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| 0.0696 | 110.9735 | 3135 | 0.9002 | 0.8175 | |
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| 0.0585 | 112.0 | 3164 | 0.9106 | 0.8025 | |
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| 0.0641 | 112.9912 | 3192 | 0.9088 | 0.81 | |
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| 0.0611 | 113.9823 | 3220 | 0.9152 | 0.8075 | |
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| 0.0528 | 114.9735 | 3248 | 0.9140 | 0.8125 | |
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| 0.0631 | 116.0 | 3277 | 0.9184 | 0.81 | |
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| 0.0744 | 116.9912 | 3305 | 0.9216 | 0.8125 | |
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| 0.0407 | 117.9823 | 3333 | 0.9211 | 0.8125 | |
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| 0.0573 | 118.9381 | 3360 | 0.9202 | 0.81 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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