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ht-stmini-cls-v6_ftis_noPretrain-msm-pos

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  1. README.md +216 -0
  2. model.safetensors +1 -1
  3. runs/0-by=2006-psr=0.25/events.out.tfevents.1747118642.ana2.178331.1 +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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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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+ model-index:
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+ - name: ht-stmini-cls-v6_ftis_noPretrain-msm-pos
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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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+ # ht-stmini-cls-v6_ftis_noPretrain-msm-pos
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.6093
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+ - Accuracy: 0.9105
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+ - Macro F1: 0.7667
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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: 0.0001
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+ - train_batch_size: 8
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 6733
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+ - training_steps: 134674
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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 | Macro F1 |
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+ |:-------------:|:--------:|:-----:|:---------------:|:--------:|:--------:|
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+ | 75.2603 | 0.0013 | 174 | 82.6528 | 0.0677 | 0.0292 |
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+ | 45.2361 | 1.0013 | 348 | 98.7323 | 0.1069 | 0.0396 |
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+ | 28.5801 | 2.0013 | 522 | 99.2570 | 0.2912 | 0.0692 |
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+ | 20.4022 | 3.0013 | 696 | 80.4975 | 0.3979 | 0.0800 |
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+ | 13.1931 | 4.0013 | 870 | 74.1763 | 0.4525 | 0.1056 |
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+ | 8.2908 | 5.0013 | 1044 | 72.5996 | 0.5100 | 0.1255 |
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+ | 5.7198 | 6.0012 | 1218 | 50.8677 | 0.5379 | 0.1312 |
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+ | 4.8925 | 7.0012 | 1392 | 53.5426 | 0.5613 | 0.1367 |
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+ | 4.1701 | 8.0012 | 1566 | 38.1837 | 0.5700 | 0.1368 |
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+ | 3.8536 | 9.0012 | 1740 | 31.5866 | 0.5925 | 0.1462 |
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+ | 3.5887 | 10.0012 | 1914 | 22.7752 | 0.5999 | 0.1510 |
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+ | 3.3238 | 11.0012 | 2088 | 19.7876 | 0.6006 | 0.1541 |
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+ | 3.185 | 12.0012 | 2262 | 15.7283 | 0.6060 | 0.1548 |
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+ | 3.2136 | 13.0012 | 2436 | 12.9876 | 0.6012 | 0.1618 |
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+ | 2.951 | 14.0012 | 2610 | 11.7802 | 0.6242 | 0.1632 |
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+ | 2.9592 | 15.0012 | 2784 | 10.4675 | 0.6244 | 0.1584 |
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+ | 2.759 | 16.0012 | 2958 | 8.9851 | 0.6199 | 0.1640 |
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+ | 2.6892 | 17.0012 | 3132 | 8.3676 | 0.5743 | 0.1596 |
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+ | 2.714 | 18.0012 | 3306 | 7.7211 | 0.6370 | 0.1805 |
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+ | 2.6502 | 19.0012 | 3480 | 7.1550 | 0.6331 | 0.1762 |
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+ | 2.5273 | 20.0011 | 3654 | 6.6970 | 0.6571 | 0.2045 |
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+ | 2.4288 | 21.0011 | 3828 | 6.1293 | 0.6377 | 0.2191 |
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+ | 2.4037 | 22.0011 | 4002 | 5.6565 | 0.6543 | 0.2191 |
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+ | 2.3739 | 23.0011 | 4176 | 5.2133 | 0.6771 | 0.2388 |
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+ | 2.2177 | 24.0011 | 4350 | 4.6704 | 0.6386 | 0.2420 |
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+ | 2.1282 | 25.0011 | 4524 | 4.8865 | 0.6728 | 0.2554 |
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+ | 1.9925 | 26.0011 | 4698 | 4.0354 | 0.7199 | 0.2987 |
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+ | 1.9449 | 27.0011 | 4872 | 4.3479 | 0.7260 | 0.3169 |
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+ | 1.8109 | 28.0011 | 5046 | 4.3502 | 0.7232 | 0.3130 |
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+ | 1.7653 | 29.0011 | 5220 | 3.9373 | 0.7319 | 0.3564 |
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+ | 1.6954 | 30.0011 | 5394 | 3.8869 | 0.7354 | 0.3425 |
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+ | 1.6546 | 31.0011 | 5568 | 3.8317 | 0.7542 | 0.3796 |
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+ | 1.5645 | 32.0011 | 5742 | 3.6349 | 0.7531 | 0.3900 |
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+ | 1.4233 | 33.0010 | 5916 | 4.0809 | 0.7687 | 0.4220 |
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+ | 1.3711 | 34.0010 | 6090 | 3.9708 | 0.7685 | 0.4136 |
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+ | 1.3536 | 35.0010 | 6264 | 3.3166 | 0.7793 | 0.4516 |
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+ | 1.2842 | 36.0010 | 6438 | 4.0544 | 0.7730 | 0.4089 |
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+ | 1.1955 | 37.0010 | 6612 | 4.2712 | 0.7833 | 0.4585 |
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+ | 1.2303 | 38.0010 | 6786 | 3.7976 | 0.7865 | 0.4792 |
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+ | 1.1236 | 39.0010 | 6960 | 3.6480 | 0.8020 | 0.4930 |
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+ | 0.985 | 40.0010 | 7134 | 3.5914 | 0.8078 | 0.4946 |
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+ | 0.9314 | 41.0010 | 7308 | 4.0045 | 0.8101 | 0.5176 |
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+ | 0.872 | 42.0010 | 7482 | 3.9282 | 0.8230 | 0.5308 |
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+ | 0.829 | 43.0010 | 7656 | 4.4537 | 0.8314 | 0.5515 |
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+ | 0.7659 | 44.0010 | 7830 | 3.8840 | 0.8330 | 0.5489 |
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+ | 0.7702 | 45.0010 | 8004 | 3.9707 | 0.8402 | 0.5444 |
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+ | 0.7176 | 46.0010 | 8178 | 4.3053 | 0.8409 | 0.5615 |
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+ | 0.6501 | 47.0009 | 8352 | 3.8825 | 0.8512 | 0.5765 |
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+ | 0.5934 | 48.0009 | 8526 | 4.3140 | 0.8522 | 0.5836 |
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+ | 0.5803 | 49.0009 | 8700 | 3.8892 | 0.8518 | 0.5939 |
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+ | 0.57 | 50.0009 | 8874 | 4.0285 | 0.8498 | 0.5787 |
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+ | 0.5901 | 51.0009 | 9048 | 4.5173 | 0.8551 | 0.5824 |
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+ | 0.5335 | 52.0009 | 9222 | 4.3036 | 0.8636 | 0.6055 |
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+ | 0.4703 | 53.0009 | 9396 | 4.1800 | 0.8564 | 0.6031 |
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+ | 0.4836 | 54.0009 | 9570 | 4.1669 | 0.8675 | 0.6185 |
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+ | 0.4621 | 55.0009 | 9744 | 5.0661 | 0.8628 | 0.6154 |
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+ | 0.3833 | 57.0009 | 10092 | 4.7826 | 0.8712 | 0.6200 |
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+ | 0.4091 | 58.0009 | 10266 | 4.5449 | 0.8736 | 0.6358 |
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+ | 0.3885 | 59.0009 | 10440 | 5.0833 | 0.8714 | 0.6294 |
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+ | 0.3673 | 60.0008 | 10614 | 4.8933 | 0.8683 | 0.6295 |
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+ | 0.3436 | 61.0008 | 10788 | 5.0857 | 0.8772 | 0.6319 |
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+ | 0.3595 | 62.0008 | 10962 | 5.4096 | 0.8778 | 0.6472 |
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+ | 0.3397 | 63.0008 | 11136 | 5.1595 | 0.8729 | 0.6333 |
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+ | 0.3073 | 64.0008 | 11310 | 4.6216 | 0.8770 | 0.6468 |
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+ | 0.297 | 65.0008 | 11484 | 4.9289 | 0.8804 | 0.6551 |
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+ | 0.2792 | 66.0008 | 11658 | 5.1199 | 0.8788 | 0.6525 |
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+ | 0.2673 | 67.0008 | 11832 | 4.6677 | 0.8855 | 0.6581 |
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+ | 0.2592 | 68.0008 | 12006 | 5.0958 | 0.8812 | 0.6613 |
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+ | 0.2632 | 69.0008 | 12180 | 4.7814 | 0.8793 | 0.6696 |
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+ | 0.2332 | 70.0008 | 12354 | 4.8025 | 0.8838 | 0.6632 |
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+ | 0.2311 | 71.0008 | 12528 | 4.2490 | 0.8867 | 0.6759 |
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+ | 0.2519 | 72.0008 | 12702 | 4.5336 | 0.8834 | 0.6677 |
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+ | 0.2197 | 73.0007 | 12876 | 4.4599 | 0.8863 | 0.6773 |
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+ | 0.2231 | 74.0007 | 13050 | 4.2366 | 0.8851 | 0.6859 |
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+ | 0.2048 | 75.0007 | 13224 | 4.2431 | 0.8879 | 0.6834 |
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+ | 0.2072 | 76.0007 | 13398 | 3.9966 | 0.8891 | 0.6852 |
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+ | 0.1801 | 78.0007 | 13746 | 3.4909 | 0.8864 | 0.6803 |
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+ | 0.1701 | 79.0007 | 13920 | 3.7384 | 0.8943 | 0.6863 |
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+ | 0.1905 | 80.0007 | 14094 | 3.5250 | 0.8896 | 0.6877 |
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+ | 0.1676 | 81.0007 | 14268 | 3.2716 | 0.8892 | 0.6941 |
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+ | 0.158 | 82.0007 | 14442 | 3.4004 | 0.8921 | 0.6988 |
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+ | 0.1473 | 83.0007 | 14616 | 3.3995 | 0.8937 | 0.7042 |
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+ | 0.1552 | 84.0007 | 14790 | 3.0094 | 0.8953 | 0.7038 |
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+ | 0.1437 | 85.0007 | 14964 | 3.6923 | 0.8945 | 0.7045 |
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+ | 0.1403 | 86.0007 | 15138 | 2.8550 | 0.8962 | 0.7016 |
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+ | 0.1487 | 87.0006 | 15312 | 3.2488 | 0.8886 | 0.6984 |
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+ | 0.1234 | 88.0006 | 15486 | 3.1374 | 0.9011 | 0.7167 |
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+ | 0.1244 | 89.0006 | 15660 | 3.4299 | 0.8917 | 0.7096 |
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+ | 0.1312 | 90.0006 | 15834 | 3.3333 | 0.8944 | 0.7136 |
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+ | 0.1209 | 91.0006 | 16008 | 3.3084 | 0.8935 | 0.7113 |
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+ | 0.1238 | 92.0006 | 16182 | 3.1446 | 0.8929 | 0.7121 |
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+ | 0.1183 | 93.0006 | 16356 | 3.1387 | 0.9008 | 0.7175 |
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+ | 0.1101 | 94.0006 | 16530 | 2.7948 | 0.9040 | 0.7287 |
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+ | 0.1109 | 95.0006 | 16704 | 2.8497 | 0.9003 | 0.7311 |
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+ | 0.1096 | 96.0006 | 16878 | 3.0141 | 0.9029 | 0.7316 |
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+ | 0.1127 | 97.0006 | 17052 | 3.0206 | 0.8989 | 0.7238 |
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+ | 0.0817 | 106.0005 | 18618 | 2.7427 | 0.9048 | 0.7338 |
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+ | 0.0683 | 116.0004 | 20358 | 2.5647 | 0.9003 | 0.7357 |
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+ | 0.0721 | 117.0004 | 20532 | 2.4017 | 0.9041 | 0.7476 |
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+ | 0.0406 | 149.0002 | 26100 | 2.6219 | 0.9072 | 0.7582 |
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+ | 0.0419 | 150.0002 | 26274 | 2.7599 | 0.9068 | 0.7528 |
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+ | 0.0427 | 151.0002 | 26448 | 2.6148 | 0.9091 | 0.7590 |
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+ | 0.0404 | 152.0002 | 26622 | 2.7172 | 0.9113 | 0.7640 |
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+ | 0.0383 | 155.0001 | 27144 | 2.9242 | 0.9095 | 0.7603 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.0
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.20.1
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