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--- |
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license: apache-2.0 |
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base_model: facebook/bart-base |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: pijarcandra22/NMTBaliIndoBART |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# pijarcandra22/NMTBaliIndoBART |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 5.4697 |
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- Validation Loss: 6.0675 |
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- Epoch: 158 |
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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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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.02, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 9.3368 | 5.6757 | 0 | |
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| 5.5627 | 5.5987 | 1 | |
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| 5.5311 | 5.5419 | 2 | |
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| 5.5152 | 5.5201 | 3 | |
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| 5.5005 | 5.6477 | 4 | |
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| 5.4704 | 5.5914 | 5 | |
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| 5.4610 | 6.0922 | 6 | |
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| 5.4584 | 5.7137 | 7 | |
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| 5.4528 | 5.8658 | 8 | |
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| 5.4820 | 5.5628 | 9 | |
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| 5.4874 | 5.5309 | 10 | |
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| 5.4917 | 5.7595 | 11 | |
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| 5.4898 | 5.7333 | 12 | |
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| 5.4833 | 5.6789 | 13 | |
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| 5.4767 | 5.9588 | 14 | |
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| 5.4883 | 5.9895 | 15 | |
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| 5.4694 | 6.0100 | 16 | |
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| 5.4663 | 6.0316 | 17 | |
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| 5.4602 | 5.9233 | 18 | |
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| 5.4576 | 6.0051 | 19 | |
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| 5.4559 | 5.9966 | 20 | |
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| 5.4651 | 6.0025 | 21 | |
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| 5.4660 | 6.0160 | 22 | |
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| 5.4626 | 5.8324 | 23 | |
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| 5.4647 | 5.8383 | 24 | |
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| 5.4695 | 6.0272 | 25 | |
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| 5.4614 | 6.0724 | 26 | |
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| 5.4623 | 5.9454 | 27 | |
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| 5.4678 | 6.0196 | 28 | |
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| 5.4860 | 5.5949 | 29 | |
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| 5.4851 | 5.8838 | 30 | |
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| 5.4666 | 5.8506 | 31 | |
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| 5.4715 | 6.0391 | 32 | |
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| 5.4630 | 6.0870 | 33 | |
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| 5.4646 | 6.2195 | 34 | |
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| 5.4574 | 5.9696 | 35 | |
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| 5.4564 | 5.8970 | 36 | |
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| 5.4570 | 5.9522 | 37 | |
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| 5.4559 | 6.1518 | 38 | |
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| 5.4584 | 6.1860 | 39 | |
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| 5.4732 | 6.1168 | 40 | |
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| 5.4625 | 6.1588 | 41 | |
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| 5.4601 | 5.9868 | 42 | |
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| 5.4645 | 5.9606 | 43 | |
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| 5.4664 | 6.1495 | 44 | |
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| 5.4698 | 6.0152 | 45 | |
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| 5.4666 | 6.2713 | 46 | |
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| 5.4557 | 6.2708 | 47 | |
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| 5.4557 | 6.0003 | 48 | |
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| 5.4693 | 5.9321 | 49 | |
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| 5.4928 | 5.8971 | 50 | |
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| 5.5032 | 6.0766 | 51 | |
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| 5.4749 | 5.8919 | 52 | |
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| 5.4689 | 5.9853 | 53 | |
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| 5.4665 | 5.9329 | 54 | |
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| 5.4574 | 5.9770 | 55 | |
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| 5.4686 | 6.1022 | 56 | |
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| 5.4727 | 5.8973 | 57 | |
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| 5.4692 | 5.9633 | 58 | |
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| 5.4608 | 6.0480 | 59 | |
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| 5.4613 | 5.9596 | 60 | |
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| 5.4607 | 6.1158 | 61 | |
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| 5.4531 | 6.0617 | 62 | |
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| 5.4610 | 6.0375 | 63 | |
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| 5.4631 | 6.1184 | 64 | |
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| 5.4627 | 6.0465 | 65 | |
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| 5.4685 | 6.0011 | 66 | |
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| 5.4642 | 6.0828 | 67 | |
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| 5.4577 | 6.0883 | 68 | |
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| 5.4615 | 5.9523 | 69 | |
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| 5.4673 | 5.7216 | 70 | |
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| 5.4724 | 6.0274 | 71 | |
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| 5.4601 | 6.0344 | 72 | |
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| 5.4640 | 5.9661 | 73 | |
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| 5.4590 | 6.0013 | 74 | |
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| 5.4622 | 6.0172 | 75 | |
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| 5.4666 | 5.8407 | 76 | |
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| 5.4669 | 6.0261 | 77 | |
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| 5.4859 | 5.9295 | 78 | |
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| 5.5042 | 6.1254 | 79 | |
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| 5.4845 | 5.8930 | 80 | |
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| 5.5001 | 5.8867 | 81 | |
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| 5.4923 | 5.9480 | 82 | |
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| 5.4909 | 6.0475 | 83 | |
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| 5.4780 | 5.9289 | 84 | |
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| 5.4867 | 5.8134 | 85 | |
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| 5.4877 | 6.0032 | 86 | |
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| 5.4806 | 6.0884 | 87 | |
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| 5.4784 | 6.0567 | 88 | |
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| 5.4830 | 5.9790 | 89 | |
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| 5.4894 | 5.8919 | 90 | |
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| 5.4890 | 5.9626 | 91 | |
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| 5.4774 | 6.0267 | 92 | |
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| 5.5033 | 6.1150 | 93 | |
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| 5.4765 | 5.9776 | 94 | |
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| 5.4657 | 6.1395 | 95 | |
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| 5.4720 | 5.9938 | 96 | |
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| 5.4748 | 5.9656 | 97 | |
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| 5.4701 | 6.0163 | 98 | |
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| 5.4718 | 6.1462 | 99 | |
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| 5.4672 | 6.0804 | 100 | |
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| 5.4775 | 6.1055 | 101 | |
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| 5.4775 | 6.0936 | 102 | |
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| 5.4673 | 5.9839 | 103 | |
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| 5.4691 | 5.8972 | 104 | |
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| 5.4694 | 5.8271 | 105 | |
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| 5.5106 | 5.5305 | 106 | |
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| 5.5135 | 5.8806 | 107 | |
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| 5.4786 | 6.1380 | 108 | |
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| 5.4770 | 5.9899 | 109 | |
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| 5.4709 | 6.1072 | 110 | |
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| 5.4701 | 5.9356 | 111 | |
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| 5.4636 | 5.8304 | 112 | |
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| 5.4670 | 6.0451 | 113 | |
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| 5.4598 | 6.0311 | 114 | |
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| 5.4731 | 5.9862 | 115 | |
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| 5.4798 | 5.9589 | 116 | |
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| 5.4674 | 5.9356 | 117 | |
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| 5.4634 | 6.0088 | 118 | |
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| 5.4709 | 5.9534 | 119 | |
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| 5.4891 | 5.9995 | 120 | |
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| 5.4737 | 5.8611 | 121 | |
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| 5.4725 | 6.0112 | 122 | |
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| 5.4835 | 5.6280 | 123 | |
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| 5.5217 | 5.6917 | 124 | |
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| 5.4821 | 5.9458 | 125 | |
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| 5.4898 | 5.7593 | 126 | |
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| 5.4866 | 5.9110 | 127 | |
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| 5.4744 | 5.9463 | 128 | |
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| 5.4673 | 6.0359 | 129 | |
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| 5.4838 | 6.0166 | 130 | |
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| 5.4864 | 6.0046 | 131 | |
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| 5.4896 | 5.9479 | 132 | |
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| 5.4722 | 6.0699 | 133 | |
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| 5.4627 | 6.0684 | 134 | |
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| 5.4690 | 6.0577 | 135 | |
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| 5.4666 | 6.1473 | 136 | |
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| 5.4655 | 6.0441 | 137 | |
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| 5.4665 | 5.9313 | 138 | |
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| 5.4588 | 6.1375 | 139 | |
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| 5.4575 | 6.1655 | 140 | |
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| 5.4609 | 5.9701 | 141 | |
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| 5.4666 | 6.0677 | 142 | |
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| 5.4672 | 6.1272 | 143 | |
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| 5.4776 | 6.2186 | 144 | |
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| 5.4769 | 5.9815 | 145 | |
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| 5.4666 | 6.0674 | 146 | |
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| 5.4670 | 6.0282 | 147 | |
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| 5.4868 | 5.7416 | 148 | |
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| 5.4901 | 6.0836 | 149 | |
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| 5.4877 | 5.9086 | 150 | |
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| 5.4842 | 5.8724 | 151 | |
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| 5.5167 | 5.7298 | 152 | |
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| 5.5043 | 5.7802 | 153 | |
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| 5.4737 | 6.0805 | 154 | |
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| 5.4805 | 6.0888 | 155 | |
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| 5.4765 | 5.9967 | 156 | |
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| 5.4691 | 5.9332 | 157 | |
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| 5.4697 | 6.0675 | 158 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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