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--- |
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base_model: aubmindlab/bert-base-arabertv02 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: arabert_cross_relevance_task7_fold6 |
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results: [] |
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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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# arabert_cross_relevance_task7_fold6 |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4397 |
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- Qwk: 0.1488 |
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- Mse: 0.4393 |
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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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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| No log | 0.125 | 2 | 0.7246 | 0.0092 | 0.7226 | |
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| No log | 0.25 | 4 | 0.3923 | 0.1169 | 0.3915 | |
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| No log | 0.375 | 6 | 0.3543 | 0.0420 | 0.3537 | |
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| No log | 0.5 | 8 | 0.3794 | 0.1151 | 0.3788 | |
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| No log | 0.625 | 10 | 0.3085 | 0.0822 | 0.3084 | |
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| No log | 0.75 | 12 | 0.2791 | 0.0429 | 0.2796 | |
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| No log | 0.875 | 14 | 0.2793 | 0.0732 | 0.2799 | |
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| No log | 1.0 | 16 | 0.2798 | 0.0822 | 0.2804 | |
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| No log | 1.125 | 18 | 0.2748 | 0.1450 | 0.2754 | |
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| No log | 1.25 | 20 | 0.2618 | 0.1769 | 0.2623 | |
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| No log | 1.375 | 22 | 0.2610 | 0.2119 | 0.2614 | |
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| No log | 1.5 | 24 | 0.2714 | 0.2099 | 0.2716 | |
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| No log | 1.625 | 26 | 0.2877 | 0.2282 | 0.2877 | |
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| No log | 1.75 | 28 | 0.3085 | 0.2170 | 0.3085 | |
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| No log | 1.875 | 30 | 0.2818 | 0.2521 | 0.2819 | |
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| No log | 2.0 | 32 | 0.2704 | 0.2285 | 0.2706 | |
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| No log | 2.125 | 34 | 0.2630 | 0.2177 | 0.2635 | |
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| No log | 2.25 | 36 | 0.2577 | 0.1930 | 0.2582 | |
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| No log | 2.375 | 38 | 0.2575 | 0.2186 | 0.2579 | |
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| No log | 2.5 | 40 | 0.2613 | 0.2282 | 0.2617 | |
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| No log | 2.625 | 42 | 0.2766 | 0.2044 | 0.2769 | |
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| No log | 2.75 | 44 | 0.2809 | 0.2091 | 0.2811 | |
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| No log | 2.875 | 46 | 0.2689 | 0.2194 | 0.2691 | |
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| No log | 3.0 | 48 | 0.2629 | 0.2198 | 0.2632 | |
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| No log | 3.125 | 50 | 0.2671 | 0.2133 | 0.2672 | |
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| No log | 3.25 | 52 | 0.2896 | 0.2170 | 0.2895 | |
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| No log | 3.375 | 54 | 0.3099 | 0.2118 | 0.3097 | |
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| No log | 3.5 | 56 | 0.2867 | 0.2181 | 0.2867 | |
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| No log | 3.625 | 58 | 0.2681 | 0.2108 | 0.2683 | |
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| No log | 3.75 | 60 | 0.2670 | 0.2042 | 0.2674 | |
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| No log | 3.875 | 62 | 0.2777 | 0.2220 | 0.2780 | |
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| No log | 4.0 | 64 | 0.2823 | 0.2220 | 0.2826 | |
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| No log | 4.125 | 66 | 0.2925 | 0.2150 | 0.2927 | |
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| No log | 4.25 | 68 | 0.3165 | 0.2285 | 0.3164 | |
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| No log | 4.375 | 70 | 0.3340 | 0.2156 | 0.3337 | |
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| No log | 4.5 | 72 | 0.3690 | 0.1876 | 0.3685 | |
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| No log | 4.625 | 74 | 0.3812 | 0.1800 | 0.3806 | |
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| No log | 4.75 | 76 | 0.3599 | 0.1954 | 0.3594 | |
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| No log | 4.875 | 78 | 0.3297 | 0.2097 | 0.3294 | |
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| No log | 5.0 | 80 | 0.3082 | 0.2063 | 0.3081 | |
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| No log | 5.125 | 82 | 0.2990 | 0.2193 | 0.2991 | |
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| No log | 5.25 | 84 | 0.3004 | 0.2193 | 0.3005 | |
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| No log | 5.375 | 86 | 0.3010 | 0.2140 | 0.3011 | |
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| No log | 5.5 | 88 | 0.3297 | 0.1814 | 0.3298 | |
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| No log | 5.625 | 90 | 0.3943 | 0.1785 | 0.3943 | |
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| No log | 5.75 | 92 | 0.4229 | 0.1592 | 0.4228 | |
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| No log | 5.875 | 94 | 0.3892 | 0.1719 | 0.3892 | |
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| No log | 6.0 | 96 | 0.3418 | 0.1836 | 0.3418 | |
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| No log | 6.125 | 98 | 0.3361 | 0.1932 | 0.3360 | |
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| No log | 6.25 | 100 | 0.3641 | 0.1930 | 0.3638 | |
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| No log | 6.375 | 102 | 0.3934 | 0.1844 | 0.3930 | |
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| No log | 6.5 | 104 | 0.3956 | 0.1915 | 0.3953 | |
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| No log | 6.625 | 106 | 0.3700 | 0.2006 | 0.3700 | |
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| No log | 6.75 | 108 | 0.3683 | 0.1800 | 0.3684 | |
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| No log | 6.875 | 110 | 0.3757 | 0.1785 | 0.3758 | |
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| No log | 7.0 | 112 | 0.4066 | 0.1688 | 0.4066 | |
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| No log | 7.125 | 114 | 0.4152 | 0.1695 | 0.4151 | |
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| No log | 7.25 | 116 | 0.3917 | 0.1790 | 0.3917 | |
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| No log | 7.375 | 118 | 0.3540 | 0.1833 | 0.3542 | |
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| No log | 7.5 | 120 | 0.3449 | 0.1998 | 0.3451 | |
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| No log | 7.625 | 122 | 0.3587 | 0.1955 | 0.3588 | |
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| No log | 7.75 | 124 | 0.3969 | 0.1807 | 0.3968 | |
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| No log | 7.875 | 126 | 0.4580 | 0.1405 | 0.4577 | |
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| No log | 8.0 | 128 | 0.5018 | 0.1433 | 0.5013 | |
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| No log | 8.125 | 130 | 0.5002 | 0.1433 | 0.4997 | |
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| No log | 8.25 | 132 | 0.4689 | 0.1442 | 0.4684 | |
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| No log | 8.375 | 134 | 0.4386 | 0.1488 | 0.4382 | |
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| No log | 8.5 | 136 | 0.4174 | 0.1550 | 0.4171 | |
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| No log | 8.625 | 138 | 0.4152 | 0.1613 | 0.4149 | |
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| No log | 8.75 | 140 | 0.4293 | 0.1550 | 0.4290 | |
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| No log | 8.875 | 142 | 0.4512 | 0.1427 | 0.4508 | |
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| No log | 9.0 | 144 | 0.4641 | 0.1405 | 0.4637 | |
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| No log | 9.125 | 146 | 0.4566 | 0.1427 | 0.4562 | |
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| No log | 9.25 | 148 | 0.4432 | 0.1427 | 0.4429 | |
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| No log | 9.375 | 150 | 0.4330 | 0.1550 | 0.4327 | |
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| No log | 9.5 | 152 | 0.4301 | 0.1613 | 0.4298 | |
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| No log | 9.625 | 154 | 0.4343 | 0.1550 | 0.4340 | |
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| No log | 9.75 | 156 | 0.4358 | 0.1550 | 0.4354 | |
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| No log | 9.875 | 158 | 0.4385 | 0.1488 | 0.4381 | |
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| No log | 10.0 | 160 | 0.4397 | 0.1488 | 0.4393 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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