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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_task2_fold4 |
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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_task2_fold4 |
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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: 0.4006 |
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- Qwk: 0.2250 |
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- Mse: 0.4006 |
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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 | 1.1063 | 0.0234 | 1.1063 | |
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| No log | 0.25 | 4 | 0.4473 | 0.1039 | 0.4473 | |
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| No log | 0.375 | 6 | 0.4088 | 0.2073 | 0.4088 | |
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| No log | 0.5 | 8 | 0.4319 | 0.2351 | 0.4319 | |
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| No log | 0.625 | 10 | 0.2875 | 0.2401 | 0.2875 | |
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| No log | 0.75 | 12 | 0.2836 | 0.2194 | 0.2836 | |
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| No log | 0.875 | 14 | 0.3021 | 0.2575 | 0.3021 | |
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| No log | 1.0 | 16 | 0.2942 | 0.2831 | 0.2942 | |
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| No log | 1.125 | 18 | 0.3155 | 0.2508 | 0.3155 | |
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| No log | 1.25 | 20 | 0.2964 | 0.2787 | 0.2964 | |
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| No log | 1.375 | 22 | 0.2774 | 0.2820 | 0.2774 | |
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| No log | 1.5 | 24 | 0.2398 | 0.3029 | 0.2398 | |
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| No log | 1.625 | 26 | 0.2524 | 0.2501 | 0.2524 | |
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| No log | 1.75 | 28 | 0.2680 | 0.2390 | 0.2680 | |
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| No log | 1.875 | 30 | 0.2581 | 0.2579 | 0.2581 | |
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| No log | 2.0 | 32 | 0.2540 | 0.2787 | 0.2540 | |
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| No log | 2.125 | 34 | 0.2836 | 0.3308 | 0.2836 | |
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| No log | 2.25 | 36 | 0.3870 | 0.2114 | 0.3870 | |
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| No log | 2.375 | 38 | 0.4205 | 0.2269 | 0.4205 | |
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| No log | 2.5 | 40 | 0.3210 | 0.2236 | 0.3210 | |
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| No log | 2.625 | 42 | 0.2478 | 0.4116 | 0.2478 | |
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| No log | 2.75 | 44 | 0.2382 | 0.3767 | 0.2382 | |
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| No log | 2.875 | 46 | 0.2494 | 0.2579 | 0.2494 | |
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| No log | 3.0 | 48 | 0.3000 | 0.2553 | 0.3000 | |
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| No log | 3.125 | 50 | 0.3533 | 0.2028 | 0.3533 | |
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| No log | 3.25 | 52 | 0.3681 | 0.2209 | 0.3681 | |
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| No log | 3.375 | 54 | 0.3235 | 0.2270 | 0.3235 | |
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| No log | 3.5 | 56 | 0.2844 | 0.2648 | 0.2844 | |
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| No log | 3.625 | 58 | 0.2941 | 0.2428 | 0.2941 | |
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| No log | 3.75 | 60 | 0.3737 | 0.2222 | 0.3737 | |
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| No log | 3.875 | 62 | 0.4306 | 0.2146 | 0.4306 | |
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| No log | 4.0 | 64 | 0.4279 | 0.2146 | 0.4279 | |
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| No log | 4.125 | 66 | 0.3959 | 0.2155 | 0.3959 | |
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| No log | 4.25 | 68 | 0.3136 | 0.2441 | 0.3136 | |
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| No log | 4.375 | 70 | 0.2972 | 0.2593 | 0.2972 | |
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| No log | 4.5 | 72 | 0.2889 | 0.2677 | 0.2889 | |
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| No log | 4.625 | 74 | 0.3100 | 0.2489 | 0.3100 | |
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| No log | 4.75 | 76 | 0.3614 | 0.2425 | 0.3614 | |
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| No log | 4.875 | 78 | 0.3949 | 0.2337 | 0.3949 | |
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| No log | 5.0 | 80 | 0.4746 | 0.1752 | 0.4746 | |
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| No log | 5.125 | 82 | 0.4462 | 0.1969 | 0.4462 | |
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| No log | 5.25 | 84 | 0.3424 | 0.2572 | 0.3424 | |
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| No log | 5.375 | 86 | 0.3171 | 0.2798 | 0.3171 | |
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| No log | 5.5 | 88 | 0.3687 | 0.2452 | 0.3687 | |
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| No log | 5.625 | 90 | 0.4496 | 0.1969 | 0.4496 | |
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| No log | 5.75 | 92 | 0.4134 | 0.2126 | 0.4134 | |
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| No log | 5.875 | 94 | 0.3763 | 0.2172 | 0.3763 | |
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| No log | 6.0 | 96 | 0.3924 | 0.2106 | 0.3924 | |
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| No log | 6.125 | 98 | 0.3918 | 0.2106 | 0.3918 | |
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| No log | 6.25 | 100 | 0.4398 | 0.2029 | 0.4398 | |
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| No log | 6.375 | 102 | 0.5008 | 0.1879 | 0.5008 | |
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| No log | 6.5 | 104 | 0.4898 | 0.1781 | 0.4898 | |
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| No log | 6.625 | 106 | 0.4198 | 0.2218 | 0.4198 | |
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| No log | 6.75 | 108 | 0.3476 | 0.2243 | 0.3476 | |
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| No log | 6.875 | 110 | 0.3538 | 0.2243 | 0.3538 | |
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| No log | 7.0 | 112 | 0.4262 | 0.2163 | 0.4262 | |
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| No log | 7.125 | 114 | 0.4667 | 0.1909 | 0.4667 | |
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| No log | 7.25 | 116 | 0.4562 | 0.1881 | 0.4562 | |
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| No log | 7.375 | 118 | 0.4023 | 0.2163 | 0.4023 | |
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| No log | 7.5 | 120 | 0.3664 | 0.2169 | 0.3664 | |
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| No log | 7.625 | 122 | 0.3487 | 0.2346 | 0.3487 | |
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| No log | 7.75 | 124 | 0.3715 | 0.2262 | 0.3715 | |
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| No log | 7.875 | 126 | 0.4267 | 0.2035 | 0.4267 | |
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| No log | 8.0 | 128 | 0.5126 | 0.1714 | 0.5126 | |
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| No log | 8.125 | 130 | 0.5465 | 0.1609 | 0.5465 | |
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| No log | 8.25 | 132 | 0.5097 | 0.1661 | 0.5097 | |
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| No log | 8.375 | 134 | 0.4320 | 0.2006 | 0.4320 | |
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| No log | 8.5 | 136 | 0.3786 | 0.2290 | 0.3786 | |
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| No log | 8.625 | 138 | 0.3721 | 0.2216 | 0.3721 | |
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| No log | 8.75 | 140 | 0.3867 | 0.2177 | 0.3867 | |
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| No log | 8.875 | 142 | 0.4094 | 0.2086 | 0.4094 | |
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| No log | 9.0 | 144 | 0.4304 | 0.2070 | 0.4304 | |
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| No log | 9.125 | 146 | 0.4412 | 0.2070 | 0.4412 | |
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| No log | 9.25 | 148 | 0.4352 | 0.2160 | 0.4352 | |
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| No log | 9.375 | 150 | 0.4157 | 0.2250 | 0.4157 | |
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| No log | 9.5 | 152 | 0.4008 | 0.2250 | 0.4008 | |
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| No log | 9.625 | 154 | 0.3937 | 0.2223 | 0.3937 | |
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| No log | 9.75 | 156 | 0.3953 | 0.2223 | 0.3953 | |
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| No log | 9.875 | 158 | 0.3987 | 0.2250 | 0.3987 | |
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| No log | 10.0 | 160 | 0.4006 | 0.2250 | 0.4006 | |
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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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