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--- |
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license: apache-2.0 |
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base_model: t5-small |
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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/t5Sunda2Indo |
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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/t5Sunda2Indo |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 1.6406 |
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- Validation Loss: 1.5932 |
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- Epoch: 140 |
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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': 2e-05, '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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| 3.9668 | 3.4054 | 0 | |
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| 3.5451 | 3.1460 | 1 | |
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| 3.3386 | 2.9773 | 2 | |
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| 3.1917 | 2.8549 | 3 | |
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| 3.0808 | 2.7568 | 4 | |
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| 2.9921 | 2.6780 | 5 | |
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| 2.9187 | 2.6135 | 6 | |
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| 2.8536 | 2.5547 | 7 | |
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| 2.8010 | 2.5040 | 8 | |
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| 2.7482 | 2.4601 | 9 | |
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| 2.7056 | 2.4227 | 10 | |
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| 2.6709 | 2.3870 | 11 | |
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| 2.6325 | 2.3554 | 12 | |
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| 2.6016 | 2.3233 | 13 | |
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| 2.5685 | 2.2965 | 14 | |
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| 2.5407 | 2.2710 | 15 | |
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| 2.5158 | 2.2486 | 16 | |
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| 2.4905 | 2.2248 | 17 | |
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| 2.4680 | 2.2037 | 18 | |
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| 2.4459 | 2.1856 | 19 | |
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| 2.4257 | 2.1684 | 20 | |
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| 2.4045 | 2.1495 | 21 | |
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| 2.3861 | 2.1325 | 22 | |
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| 2.3676 | 2.1189 | 23 | |
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| 2.3479 | 2.1028 | 24 | |
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| 2.3333 | 2.0873 | 25 | |
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| 2.3192 | 2.0743 | 26 | |
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| 2.3035 | 2.0647 | 27 | |
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| 2.2889 | 2.0504 | 28 | |
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| 2.2742 | 2.0395 | 29 | |
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| 2.2608 | 2.0285 | 30 | |
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| 2.2471 | 2.0166 | 31 | |
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| 2.2344 | 2.0078 | 32 | |
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| 2.2211 | 1.9999 | 33 | |
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| 2.2086 | 1.9893 | 34 | |
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| 2.1965 | 1.9790 | 35 | |
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| 2.1879 | 1.9724 | 36 | |
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| 2.1774 | 1.9637 | 37 | |
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| 2.1663 | 1.9537 | 38 | |
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| 2.1573 | 1.9461 | 39 | |
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| 2.1470 | 1.9389 | 40 | |
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| 2.1344 | 1.9329 | 41 | |
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| 2.1259 | 1.9257 | 42 | |
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| 2.1192 | 1.9158 | 43 | |
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| 2.1098 | 1.9092 | 44 | |
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| 2.0993 | 1.9021 | 45 | |
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| 2.0930 | 1.8970 | 46 | |
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| 2.0831 | 1.8909 | 47 | |
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| 2.0729 | 1.8845 | 48 | |
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| 2.0669 | 1.8799 | 49 | |
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| 2.0587 | 1.8746 | 50 | |
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| 2.0519 | 1.8662 | 51 | |
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| 2.0446 | 1.8605 | 52 | |
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| 2.0338 | 1.8552 | 53 | |
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| 2.0297 | 1.8494 | 54 | |
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| 2.0207 | 1.8441 | 55 | |
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| 2.0151 | 1.8404 | 56 | |
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| 2.0116 | 1.8346 | 57 | |
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| 2.0029 | 1.8286 | 58 | |
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| 1.9942 | 1.8243 | 59 | |
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| 1.9894 | 1.8177 | 60 | |
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| 1.9820 | 1.8145 | 61 | |
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| 1.9753 | 1.8100 | 62 | |
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| 1.9698 | 1.8054 | 63 | |
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| 1.9635 | 1.8001 | 64 | |
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| 1.9588 | 1.7963 | 65 | |
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| 1.9533 | 1.7895 | 66 | |
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| 1.9443 | 1.7888 | 67 | |
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| 1.9381 | 1.7846 | 68 | |
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| 1.9320 | 1.7806 | 69 | |
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| 1.9281 | 1.7755 | 70 | |
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| 1.9232 | 1.7697 | 71 | |
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| 1.9141 | 1.7672 | 72 | |
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| 1.9128 | 1.7655 | 73 | |
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| 1.9041 | 1.7611 | 74 | |
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| 1.8987 | 1.7558 | 75 | |
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| 1.8955 | 1.7498 | 76 | |
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| 1.8874 | 1.7493 | 77 | |
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| 1.8845 | 1.7433 | 78 | |
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| 1.8793 | 1.7403 | 79 | |
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| 1.8723 | 1.7370 | 80 | |
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| 1.8669 | 1.7319 | 81 | |
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| 1.8626 | 1.7323 | 82 | |
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| 1.8593 | 1.7268 | 83 | |
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| 1.8540 | 1.7235 | 84 | |
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| 1.8504 | 1.7204 | 85 | |
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| 1.8468 | 1.7180 | 86 | |
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| 1.8398 | 1.7130 | 87 | |
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| 1.8358 | 1.7088 | 88 | |
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| 1.8321 | 1.7081 | 89 | |
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| 1.8271 | 1.7042 | 90 | |
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| 1.8203 | 1.7016 | 91 | |
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| 1.8189 | 1.6985 | 92 | |
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| 1.8117 | 1.6967 | 93 | |
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| 1.8089 | 1.6924 | 94 | |
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| 1.8035 | 1.6898 | 95 | |
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| 1.7984 | 1.6904 | 96 | |
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| 1.7957 | 1.6836 | 97 | |
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| 1.7925 | 1.6833 | 98 | |
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| 1.7880 | 1.6786 | 99 | |
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| 1.7816 | 1.6770 | 100 | |
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| 1.7772 | 1.6739 | 101 | |
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| 1.7754 | 1.6733 | 102 | |
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| 1.7712 | 1.6678 | 103 | |
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| 1.7677 | 1.6638 | 104 | |
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| 1.7641 | 1.6627 | 105 | |
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| 1.7601 | 1.6609 | 106 | |
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| 1.7573 | 1.6585 | 107 | |
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| 1.7516 | 1.6559 | 108 | |
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| 1.7478 | 1.6523 | 109 | |
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| 1.7443 | 1.6523 | 110 | |
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| 1.7406 | 1.6498 | 111 | |
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| 1.7361 | 1.6475 | 112 | |
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| 1.7326 | 1.6435 | 113 | |
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| 1.7285 | 1.6422 | 114 | |
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| 1.7244 | 1.6398 | 115 | |
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| 1.7205 | 1.6386 | 116 | |
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| 1.7187 | 1.6347 | 117 | |
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| 1.7157 | 1.6335 | 118 | |
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| 1.7113 | 1.6317 | 119 | |
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| 1.7094 | 1.6308 | 120 | |
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| 1.7074 | 1.6267 | 121 | |
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| 1.7007 | 1.6252 | 122 | |
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| 1.6965 | 1.6241 | 123 | |
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| 1.6931 | 1.6231 | 124 | |
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| 1.6913 | 1.6201 | 125 | |
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| 1.6862 | 1.6174 | 126 | |
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| 1.6833 | 1.6176 | 127 | |
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| 1.6827 | 1.6122 | 128 | |
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| 1.6774 | 1.6127 | 129 | |
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| 1.6726 | 1.6119 | 130 | |
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| 1.6698 | 1.6079 | 131 | |
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| 1.6663 | 1.6077 | 132 | |
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| 1.6631 | 1.6055 | 133 | |
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| 1.6617 | 1.6043 | 134 | |
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| 1.6573 | 1.6019 | 135 | |
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| 1.6549 | 1.5994 | 136 | |
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| 1.6514 | 1.5990 | 137 | |
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| 1.6484 | 1.5965 | 138 | |
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| 1.6490 | 1.5942 | 139 | |
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| 1.6406 | 1.5932 | 140 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.14.0 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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