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swin-transformer3

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swin
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: microsoft/swin-large-patch4-window12-384
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: swin-transformer3
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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-transformer3
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+
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+ This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384](https://huggingface.co/microsoft/swin-large-patch4-window12-384) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 4.1081
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+ - Accuracy: 0.5667
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+ - F1: 0.5667
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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: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 16
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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: 100
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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 | F1 |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
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+ | 1.6659 | 0.9925 | 33 | 1.0639 | 0.6333 | 0.6250 |
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+ | 0.7561 | 1.9850 | 66 | 0.7258 | 0.5167 | 0.3520 |
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+ | 0.7106 | 2.9774 | 99 | 0.7334 | 0.5 | 0.3755 |
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+ | 0.6749 | 4.0 | 133 | 0.7088 | 0.4833 | 0.3661 |
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+ | 0.751 | 4.9925 | 166 | 0.7356 | 0.4833 | 0.3661 |
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+ | 0.7146 | 5.9850 | 199 | 0.7837 | 0.4833 | 0.3150 |
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+ | 0.6699 | 6.9774 | 232 | 0.7569 | 0.4833 | 0.3424 |
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+ | 0.6521 | 8.0 | 266 | 0.7255 | 0.5333 | 0.4674 |
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+ | 0.6885 | 8.9925 | 299 | 0.7253 | 0.5167 | 0.4070 |
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+ | 0.6407 | 9.9850 | 332 | 0.6506 | 0.6 | 0.5909 |
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+ | 0.6436 | 10.9774 | 365 | 0.6720 | 0.55 | 0.4442 |
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+ | 0.7865 | 12.0 | 399 | 0.6606 | 0.55 | 0.4792 |
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+ | 0.7191 | 12.9925 | 432 | 0.6407 | 0.65 | 0.6466 |
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+ | 0.5889 | 13.9850 | 465 | 0.8008 | 0.4833 | 0.3619 |
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+ | 0.5489 | 14.9774 | 498 | 0.7298 | 0.5333 | 0.4674 |
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+ | 0.596 | 16.0 | 532 | 0.7465 | 0.6667 | 0.6591 |
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+ | 0.6136 | 16.9925 | 565 | 0.9118 | 0.5333 | 0.4692 |
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+ | 0.5961 | 17.9850 | 598 | 0.6902 | 0.65 | 0.6298 |
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+ | 0.6327 | 18.9774 | 631 | 0.8260 | 0.5667 | 0.5190 |
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+ | 0.6518 | 20.0 | 665 | 0.6919 | 0.5833 | 0.5715 |
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+ | 0.5551 | 20.9925 | 698 | 1.1780 | 0.55 | 0.516 |
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+ | 0.511 | 21.9850 | 731 | 0.7414 | 0.6 | 0.6 |
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+ | 0.4749 | 22.9774 | 764 | 0.7978 | 0.6167 | 0.6129 |
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+ | 0.4607 | 24.0 | 798 | 0.8087 | 0.55 | 0.5420 |
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+ | 0.5837 | 24.9925 | 831 | 0.8271 | 0.5667 | 0.5456 |
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+ | 0.4608 | 25.9850 | 864 | 0.8539 | 0.6 | 0.5863 |
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+ | 0.536 | 26.9774 | 897 | 0.9802 | 0.5333 | 0.5026 |
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+ | 0.4225 | 28.0 | 931 | 0.9275 | 0.6 | 0.5910 |
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+ | 0.4325 | 28.9925 | 964 | 0.8834 | 0.6167 | 0.6099 |
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+ | 0.4874 | 29.9850 | 997 | 0.8721 | 0.6167 | 0.6168 |
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+ | 0.4165 | 30.9774 | 1030 | 1.0360 | 0.6167 | 0.6163 |
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+ | 0.4773 | 32.0 | 1064 | 1.2210 | 0.5833 | 0.5759 |
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+ | 0.3756 | 32.9925 | 1097 | 1.1291 | 0.5833 | 0.5830 |
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+ | 0.636 | 33.9850 | 1130 | 1.0178 | 0.5833 | 0.5830 |
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+ | 0.5474 | 34.9774 | 1163 | 0.9479 | 0.5667 | 0.5608 |
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+ | 0.3462 | 36.0 | 1197 | 0.9585 | 0.6167 | 0.6163 |
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+ | 0.3057 | 36.9925 | 1230 | 1.2014 | 0.6167 | 0.6163 |
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+ | 0.2304 | 37.9850 | 1263 | 1.1975 | 0.6333 | 0.6333 |
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+ | 0.2628 | 38.9774 | 1296 | 1.5224 | 0.5833 | 0.5793 |
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+ | 0.3774 | 40.0 | 1330 | 1.2903 | 0.5667 | 0.5516 |
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+ | 0.2604 | 40.9925 | 1363 | 1.4082 | 0.5667 | 0.5608 |
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+ | 0.2522 | 41.9850 | 1396 | 1.1783 | 0.6167 | 0.6163 |
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+ | 0.1925 | 42.9774 | 1429 | 1.3613 | 0.6167 | 0.6163 |
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+ | 0.3436 | 44.0 | 1463 | 1.6383 | 0.5333 | 0.5173 |
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+ | 0.1955 | 44.9925 | 1496 | 1.8947 | 0.5 | 0.4829 |
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+ | 0.2206 | 45.9850 | 1529 | 1.4390 | 0.6 | 0.6 |
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+ | 0.1912 | 46.9774 | 1562 | 1.5288 | 0.65 | 0.6400 |
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+ | 0.2794 | 48.0 | 1596 | 1.7393 | 0.55 | 0.5420 |
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+ | 0.3166 | 48.9925 | 1629 | 2.0414 | 0.5667 | 0.5608 |
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+ | 0.173 | 49.9850 | 1662 | 1.6377 | 0.6 | 0.5991 |
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+ | 0.1375 | 50.9774 | 1695 | 1.6228 | 0.6 | 0.6 |
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+ | 0.2659 | 52.0 | 1729 | 1.6452 | 0.6333 | 0.6333 |
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+ | 0.2045 | 52.9925 | 1762 | 1.9706 | 0.5667 | 0.5608 |
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+ | 0.1081 | 53.9850 | 1795 | 1.9546 | 0.6167 | 0.6009 |
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+ | 0.1782 | 54.9774 | 1828 | 2.1268 | 0.5667 | 0.5608 |
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+ | 0.244 | 56.0 | 1862 | 1.8301 | 0.6167 | 0.6098 |
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+ | 0.1783 | 56.9925 | 1895 | 2.5808 | 0.5667 | 0.5071 |
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+ | 0.2429 | 57.9850 | 1928 | 2.1214 | 0.6167 | 0.6059 |
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+ | 0.2 | 58.9774 | 1961 | 2.2282 | 0.5667 | 0.5657 |
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+ | 0.1646 | 60.0 | 1995 | 2.3272 | 0.5833 | 0.5662 |
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+ | 0.1663 | 60.9925 | 2028 | 2.4723 | 0.5333 | 0.5323 |
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+ | 0.1935 | 61.9850 | 2061 | 2.3384 | 0.6 | 0.5973 |
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+ | 0.2079 | 62.9774 | 2094 | 1.9271 | 0.5833 | 0.5830 |
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+ | 0.1797 | 64.0 | 2128 | 1.8707 | 0.6167 | 0.6151 |
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+ | 0.173 | 64.9925 | 2161 | 2.6292 | 0.5167 | 0.5031 |
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+ | 0.1815 | 65.9850 | 2194 | 2.6567 | 0.6 | 0.5973 |
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+ | 0.0665 | 66.9774 | 2227 | 3.2104 | 0.5167 | 0.5031 |
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+ | 0.1084 | 68.0 | 2261 | 3.6692 | 0.5333 | 0.5228 |
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+ | 0.1298 | 68.9925 | 2294 | 3.4104 | 0.55 | 0.5373 |
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+ | 0.1338 | 69.9850 | 2327 | 2.8215 | 0.6 | 0.5973 |
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+ | 0.0795 | 70.9774 | 2360 | 2.9208 | 0.5833 | 0.5830 |
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+ | 0.1138 | 72.0 | 2394 | 3.4277 | 0.5333 | 0.5302 |
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+ | 0.1644 | 72.9925 | 2427 | 2.8141 | 0.5833 | 0.5830 |
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+ | 0.1659 | 73.9850 | 2460 | 2.8723 | 0.6 | 0.6 |
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+ | 0.0453 | 74.9774 | 2493 | 2.8769 | 0.6333 | 0.6309 |
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+ | 0.0956 | 76.0 | 2527 | 3.2970 | 0.6167 | 0.6098 |
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+ | 0.1581 | 76.9925 | 2560 | 3.6672 | 0.5833 | 0.5816 |
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+ | 0.157 | 77.9850 | 2593 | 3.5317 | 0.55 | 0.5501 |
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+ | 0.0662 | 78.9774 | 2626 | 3.9003 | 0.55 | 0.5456 |
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+ | 0.1954 | 80.0 | 2660 | 3.3000 | 0.5833 | 0.5834 |
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+ | 0.0527 | 80.9925 | 2693 | 3.9596 | 0.5667 | 0.5638 |
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+ | 0.1578 | 81.9850 | 2726 | 3.6724 | 0.55 | 0.5481 |
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+ | 0.0737 | 82.9774 | 2759 | 4.0222 | 0.5167 | 0.5119 |
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+ | 0.0617 | 84.0 | 2793 | 3.5510 | 0.5833 | 0.5834 |
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+ | 0.0531 | 84.9925 | 2826 | 3.5110 | 0.6 | 0.6 |
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+ | 0.0993 | 85.9850 | 2859 | 4.0699 | 0.55 | 0.5481 |
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+ | 0.1545 | 86.9774 | 2892 | 3.6860 | 0.5667 | 0.5667 |
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+ | 0.0554 | 88.0 | 2926 | 3.4409 | 0.6 | 0.6 |
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+ | 0.0641 | 88.9925 | 2959 | 3.8304 | 0.55 | 0.5496 |
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+ | 0.0633 | 89.9850 | 2992 | 4.0899 | 0.55 | 0.5456 |
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+ | 0.0991 | 90.9774 | 3025 | 3.7344 | 0.6 | 0.6 |
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+ | 0.0772 | 92.0 | 3059 | 3.8448 | 0.6 | 0.5991 |
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+ | 0.0646 | 92.9925 | 3092 | 3.7794 | 0.6 | 0.5991 |
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+ | 0.0562 | 93.9850 | 3125 | 3.9340 | 0.5833 | 0.5830 |
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+ | 0.0475 | 94.9774 | 3158 | 4.2388 | 0.55 | 0.5481 |
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+ | 0.0715 | 96.0 | 3192 | 4.2732 | 0.5333 | 0.5302 |
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+ | 0.0875 | 96.9925 | 3225 | 4.1521 | 0.5667 | 0.5657 |
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+ | 0.0253 | 97.9850 | 3258 | 4.0813 | 0.5667 | 0.5667 |
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+ | 0.1037 | 98.9774 | 3291 | 4.1074 | 0.5667 | 0.5667 |
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+ | 0.1094 | 99.2481 | 3300 | 4.1081 | 0.5667 | 0.5667 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.1
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