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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/NMTIndoBaliT5 |
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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/NMTIndoBaliT5 |
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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: 0.1252 |
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- Validation Loss: 2.2681 |
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- Epoch: 229 |
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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': 1e-04, '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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| 3.2881 | 2.6852 | 0 | |
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| 2.7514 | 2.4004 | 1 | |
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| 2.5012 | 2.2171 | 2 | |
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| 2.3252 | 2.0959 | 3 | |
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| 2.1930 | 1.9901 | 4 | |
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| 2.0837 | 1.9130 | 5 | |
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| 1.9912 | 1.8452 | 6 | |
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| 1.9107 | 1.7974 | 7 | |
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| 1.8459 | 1.7521 | 8 | |
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| 1.7902 | 1.7165 | 9 | |
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| 1.7321 | 1.6842 | 10 | |
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| 1.6811 | 1.6400 | 11 | |
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| 1.6374 | 1.6230 | 12 | |
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| 1.5973 | 1.5960 | 13 | |
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| 1.5588 | 1.5765 | 14 | |
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| 1.5244 | 1.5589 | 15 | |
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| 1.4933 | 1.5370 | 16 | |
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| 1.4588 | 1.5300 | 17 | |
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| 1.4325 | 1.5107 | 18 | |
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| 1.4054 | 1.4970 | 19 | |
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| 1.3730 | 1.4839 | 20 | |
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| 1.3475 | 1.4789 | 21 | |
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| 1.3231 | 1.4616 | 22 | |
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| 1.3035 | 1.4568 | 23 | |
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| 1.2768 | 1.4489 | 24 | |
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| 1.2587 | 1.4396 | 25 | |
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| 1.2380 | 1.4364 | 26 | |
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| 1.2208 | 1.4273 | 27 | |
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| 1.2026 | 1.4228 | 28 | |
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| 1.1755 | 1.4141 | 29 | |
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| 1.1614 | 1.4062 | 30 | |
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| 1.1460 | 1.4060 | 31 | |
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| 1.1289 | 1.3934 | 32 | |
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| 1.1134 | 1.4007 | 33 | |
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| 1.0965 | 1.3927 | 34 | |
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| 1.0818 | 1.3874 | 35 | |
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| 1.0661 | 1.3921 | 36 | |
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| 1.0482 | 1.3795 | 37 | |
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| 1.0345 | 1.3853 | 38 | |
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| 1.0195 | 1.3835 | 39 | |
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| 1.0074 | 1.3772 | 40 | |
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| 0.9890 | 1.3851 | 41 | |
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| 0.9833 | 1.3724 | 42 | |
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| 0.9667 | 1.3740 | 43 | |
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| 0.9561 | 1.3752 | 44 | |
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| 0.9429 | 1.3673 | 45 | |
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| 0.9301 | 1.3828 | 46 | |
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| 0.9141 | 1.3806 | 47 | |
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| 0.9050 | 1.3772 | 48 | |
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| 0.8952 | 1.3812 | 49 | |
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| 0.8809 | 1.3718 | 50 | |
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| 0.8725 | 1.3825 | 51 | |
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| 0.8601 | 1.3842 | 52 | |
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| 0.8488 | 1.3827 | 53 | |
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| 0.8375 | 1.3920 | 54 | |
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| 0.8257 | 1.3936 | 55 | |
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| 0.8184 | 1.3842 | 56 | |
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| 0.8081 | 1.3846 | 57 | |
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| 0.7986 | 1.3860 | 58 | |
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| 0.7883 | 1.3943 | 59 | |
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| 0.7787 | 1.4004 | 60 | |
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| 0.7666 | 1.4071 | 61 | |
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| 0.7554 | 1.4079 | 62 | |
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| 0.7470 | 1.4038 | 63 | |
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| 0.7366 | 1.4141 | 64 | |
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| 0.7279 | 1.4135 | 65 | |
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| 0.7250 | 1.4111 | 66 | |
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| 0.7128 | 1.4196 | 67 | |
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| 0.7042 | 1.4182 | 68 | |
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| 0.6946 | 1.4378 | 69 | |
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| 0.6851 | 1.4350 | 70 | |
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| 0.6764 | 1.4403 | 71 | |
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| 0.6695 | 1.4474 | 72 | |
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| 0.6606 | 1.4454 | 73 | |
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| 0.6565 | 1.4516 | 74 | |
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| 0.6450 | 1.4595 | 75 | |
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| 0.6347 | 1.4700 | 76 | |
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| 0.6287 | 1.4746 | 77 | |
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| 0.6183 | 1.4813 | 78 | |
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| 0.6143 | 1.4785 | 79 | |
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| 0.6053 | 1.4848 | 80 | |
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| 0.5994 | 1.4777 | 81 | |
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| 0.5903 | 1.4962 | 82 | |
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| 0.5828 | 1.5102 | 83 | |
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| 0.5760 | 1.4957 | 84 | |
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| 0.5696 | 1.5121 | 85 | |
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| 0.5637 | 1.5168 | 86 | |
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| 0.5578 | 1.5183 | 87 | |
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| 0.5499 | 1.5184 | 88 | |
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| 0.5396 | 1.5433 | 89 | |
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| 0.5345 | 1.5411 | 90 | |
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| 0.5268 | 1.5338 | 91 | |
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| 0.5220 | 1.5556 | 92 | |
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| 0.5184 | 1.5489 | 93 | |
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| 0.5122 | 1.5635 | 94 | |
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| 0.5014 | 1.5674 | 95 | |
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| 0.4921 | 1.5773 | 96 | |
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| 0.4925 | 1.5773 | 97 | |
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| 0.4821 | 1.5938 | 98 | |
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| 0.4769 | 1.6013 | 99 | |
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| 0.4723 | 1.5979 | 100 | |
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| 0.4692 | 1.6131 | 101 | |
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| 0.4603 | 1.6247 | 102 | |
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| 0.4553 | 1.6276 | 103 | |
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| 0.4476 | 1.6376 | 104 | |
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| 0.4401 | 1.6390 | 105 | |
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| 0.4384 | 1.6442 | 106 | |
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| 0.4305 | 1.6548 | 107 | |
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| 0.4263 | 1.6617 | 108 | |
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| 0.4232 | 1.6523 | 109 | |
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| 0.4185 | 1.6561 | 110 | |
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| 0.4129 | 1.6779 | 111 | |
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| 0.4036 | 1.6897 | 112 | |
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| 0.4005 | 1.6873 | 113 | |
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| 0.3948 | 1.6987 | 114 | |
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| 0.3892 | 1.7120 | 115 | |
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| 0.3859 | 1.7049 | 116 | |
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| 0.3795 | 1.7241 | 117 | |
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| 0.3802 | 1.7273 | 118 | |
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| 0.3731 | 1.7387 | 119 | |
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| 0.3672 | 1.7447 | 120 | |
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| 0.3629 | 1.7513 | 121 | |
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| 0.3607 | 1.7515 | 122 | |
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| 0.3543 | 1.7585 | 123 | |
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| 0.3504 | 1.7601 | 124 | |
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| 0.3477 | 1.7657 | 125 | |
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| 0.3453 | 1.7733 | 126 | |
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| 0.3448 | 1.7718 | 127 | |
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| 0.3390 | 1.7971 | 128 | |
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| 0.3352 | 1.7929 | 129 | |
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| 0.3273 | 1.7988 | 130 | |
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| 0.3250 | 1.8192 | 131 | |
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| 0.3222 | 1.8220 | 132 | |
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| 0.3173 | 1.8289 | 133 | |
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| 0.3171 | 1.8261 | 134 | |
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| 0.3124 | 1.8415 | 135 | |
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| 0.3040 | 1.8379 | 136 | |
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| 0.3040 | 1.8533 | 137 | |
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| 0.3030 | 1.8511 | 138 | |
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| 0.2970 | 1.8537 | 139 | |
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| 0.2938 | 1.8697 | 140 | |
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| 0.2929 | 1.8730 | 141 | |
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| 0.2892 | 1.8632 | 142 | |
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| 0.2816 | 1.8796 | 143 | |
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| 0.2812 | 1.8870 | 144 | |
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| 0.2761 | 1.8891 | 145 | |
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| 0.2731 | 1.9134 | 146 | |
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| 0.2698 | 1.9100 | 147 | |
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| 0.2671 | 1.9207 | 148 | |
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| 0.2639 | 1.9196 | 149 | |
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| 0.2621 | 1.9130 | 150 | |
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| 0.2589 | 1.9273 | 151 | |
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| 0.2558 | 1.9336 | 152 | |
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| 0.2545 | 1.9355 | 153 | |
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| 0.2487 | 1.9551 | 154 | |
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| 0.2493 | 1.9573 | 155 | |
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| 0.2449 | 1.9552 | 156 | |
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| 0.2421 | 1.9591 | 157 | |
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| 0.2405 | 1.9556 | 158 | |
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| 0.2367 | 1.9807 | 159 | |
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| 0.2342 | 1.9859 | 160 | |
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| 0.2316 | 1.9803 | 161 | |
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| 0.2281 | 1.9853 | 162 | |
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| 0.2269 | 1.9970 | 163 | |
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| 0.2250 | 2.0120 | 164 | |
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| 0.2236 | 2.0107 | 165 | |
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| 0.2194 | 2.0208 | 166 | |
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| 0.2183 | 2.0198 | 167 | |
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| 0.2168 | 2.0265 | 168 | |
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| 0.2172 | 2.0278 | 169 | |
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| 0.2117 | 2.0380 | 170 | |
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| 0.2078 | 2.0448 | 171 | |
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| 0.2091 | 2.0415 | 172 | |
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| 0.2065 | 2.0459 | 173 | |
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| 0.2027 | 2.0597 | 174 | |
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| 0.1995 | 2.0659 | 175 | |
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| 0.1980 | 2.0811 | 176 | |
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| 0.1971 | 2.0704 | 177 | |
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| 0.1932 | 2.0785 | 178 | |
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| 0.1892 | 2.0783 | 179 | |
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| 0.1924 | 2.0742 | 180 | |
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| 0.1872 | 2.0979 | 181 | |
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| 0.1858 | 2.0958 | 182 | |
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| 0.1853 | 2.1005 | 183 | |
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| 0.1834 | 2.1166 | 184 | |
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| 0.1810 | 2.1027 | 185 | |
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| 0.1789 | 2.1151 | 186 | |
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| 0.1768 | 2.1302 | 187 | |
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| 0.1768 | 2.1200 | 188 | |
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| 0.1766 | 2.1399 | 189 | |
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| 0.1732 | 2.1196 | 190 | |
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| 0.1719 | 2.1362 | 191 | |
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| 0.1697 | 2.1447 | 192 | |
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| 0.1684 | 2.1464 | 193 | |
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| 0.1699 | 2.1442 | 194 | |
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| 0.1657 | 2.1492 | 195 | |
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| 0.1607 | 2.1644 | 196 | |
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| 0.1603 | 2.1667 | 197 | |
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| 0.1580 | 2.1715 | 198 | |
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| 0.1588 | 2.1818 | 199 | |
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| 0.1551 | 2.1825 | 200 | |
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| 0.1572 | 2.1779 | 201 | |
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| 0.1552 | 2.1842 | 202 | |
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| 0.1528 | 2.2038 | 203 | |
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| 0.1530 | 2.1941 | 204 | |
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| 0.1501 | 2.1903 | 205 | |
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| 0.1492 | 2.2089 | 206 | |
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| 0.1498 | 2.1871 | 207 | |
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| 0.1481 | 2.1888 | 208 | |
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| 0.1486 | 2.2130 | 209 | |
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| 0.1434 | 2.2259 | 210 | |
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| 0.1432 | 2.2159 | 211 | |
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| 0.1436 | 2.2151 | 212 | |
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| 0.1411 | 2.2221 | 213 | |
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| 0.1414 | 2.2294 | 214 | |
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| 0.1381 | 2.2310 | 215 | |
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| 0.1360 | 2.2444 | 216 | |
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| 0.1353 | 2.2427 | 217 | |
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| 0.1372 | 2.2461 | 218 | |
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| 0.1350 | 2.2455 | 219 | |
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| 0.1319 | 2.2616 | 220 | |
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| 0.1345 | 2.2556 | 221 | |
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| 0.1319 | 2.2567 | 222 | |
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| 0.1301 | 2.2589 | 223 | |
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| 0.1273 | 2.2709 | 224 | |
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| 0.1266 | 2.2737 | 225 | |
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| 0.1251 | 2.2794 | 226 | |
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| 0.1255 | 2.2707 | 227 | |
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| 0.1264 | 2.2903 | 228 | |
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| 0.1252 | 2.2681 | 229 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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