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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_task5_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_task5_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: 0.2257 |
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- Qwk: 0.2916 |
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- Mse: 0.2257 |
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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 | 0.5419 | 0.0637 | 0.5419 | |
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| No log | 0.25 | 4 | 0.4252 | 0.0672 | 0.4252 | |
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| No log | 0.375 | 6 | 0.4303 | 0.1252 | 0.4303 | |
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| No log | 0.5 | 8 | 0.3440 | 0.0304 | 0.3440 | |
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| No log | 0.625 | 10 | 0.3129 | 0.1293 | 0.3129 | |
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| No log | 0.75 | 12 | 0.2830 | 0.1429 | 0.2830 | |
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| No log | 0.875 | 14 | 0.2992 | 0.0958 | 0.2992 | |
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| No log | 1.0 | 16 | 0.3104 | 0.0958 | 0.3104 | |
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| No log | 1.125 | 18 | 0.2898 | 0.1779 | 0.2898 | |
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| No log | 1.25 | 20 | 0.2346 | 0.2417 | 0.2346 | |
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| No log | 1.375 | 22 | 0.2176 | 0.2728 | 0.2176 | |
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| No log | 1.5 | 24 | 0.2179 | 0.2942 | 0.2179 | |
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| No log | 1.625 | 26 | 0.2211 | 0.2728 | 0.2211 | |
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| No log | 1.75 | 28 | 0.2268 | 0.2654 | 0.2268 | |
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| No log | 1.875 | 30 | 0.2571 | 0.2062 | 0.2571 | |
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| No log | 2.0 | 32 | 0.2605 | 0.2114 | 0.2605 | |
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| No log | 2.125 | 34 | 0.2380 | 0.2417 | 0.2380 | |
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| No log | 2.25 | 36 | 0.2330 | 0.2417 | 0.2330 | |
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| No log | 2.375 | 38 | 0.2329 | 0.2372 | 0.2329 | |
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| No log | 2.5 | 40 | 0.2261 | 0.2459 | 0.2261 | |
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| No log | 2.625 | 42 | 0.2248 | 0.2728 | 0.2248 | |
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| No log | 2.75 | 44 | 0.2236 | 0.2691 | 0.2236 | |
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| No log | 2.875 | 46 | 0.2213 | 0.2800 | 0.2213 | |
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| No log | 3.0 | 48 | 0.2240 | 0.3047 | 0.2240 | |
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| No log | 3.125 | 50 | 0.2303 | 0.3788 | 0.2303 | |
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| No log | 3.25 | 52 | 0.2307 | 0.3842 | 0.2307 | |
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| No log | 3.375 | 54 | 0.2254 | 0.3366 | 0.2254 | |
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| No log | 3.5 | 56 | 0.2125 | 0.2834 | 0.2125 | |
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| No log | 3.625 | 58 | 0.2139 | 0.2575 | 0.2139 | |
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| No log | 3.75 | 60 | 0.2124 | 0.2575 | 0.2124 | |
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| No log | 3.875 | 62 | 0.2123 | 0.2575 | 0.2123 | |
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| No log | 4.0 | 64 | 0.2174 | 0.2651 | 0.2174 | |
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| No log | 4.125 | 66 | 0.2253 | 0.2881 | 0.2253 | |
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| No log | 4.25 | 68 | 0.2274 | 0.3188 | 0.2274 | |
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| No log | 4.375 | 70 | 0.2349 | 0.3309 | 0.2349 | |
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| No log | 4.5 | 72 | 0.2272 | 0.3342 | 0.2272 | |
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| No log | 4.625 | 74 | 0.2149 | 0.2951 | 0.2149 | |
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| No log | 4.75 | 76 | 0.2123 | 0.2763 | 0.2123 | |
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| No log | 4.875 | 78 | 0.2155 | 0.2654 | 0.2155 | |
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| No log | 5.0 | 80 | 0.2264 | 0.2417 | 0.2264 | |
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| No log | 5.125 | 82 | 0.2373 | 0.2450 | 0.2373 | |
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| No log | 5.25 | 84 | 0.2372 | 0.2606 | 0.2372 | |
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| No log | 5.375 | 86 | 0.2221 | 0.2836 | 0.2221 | |
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| No log | 5.5 | 88 | 0.2147 | 0.3249 | 0.2147 | |
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| No log | 5.625 | 90 | 0.2144 | 0.3301 | 0.2144 | |
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| No log | 5.75 | 92 | 0.2151 | 0.3193 | 0.2151 | |
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| No log | 5.875 | 94 | 0.2290 | 0.2528 | 0.2290 | |
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| No log | 6.0 | 96 | 0.2470 | 0.2355 | 0.2470 | |
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| No log | 6.125 | 98 | 0.2460 | 0.2355 | 0.2460 | |
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| No log | 6.25 | 100 | 0.2397 | 0.2450 | 0.2397 | |
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| No log | 6.375 | 102 | 0.2306 | 0.2528 | 0.2306 | |
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| No log | 6.5 | 104 | 0.2229 | 0.2951 | 0.2229 | |
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| No log | 6.625 | 106 | 0.2219 | 0.3217 | 0.2219 | |
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| No log | 6.75 | 108 | 0.2245 | 0.3543 | 0.2245 | |
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| No log | 6.875 | 110 | 0.2279 | 0.3412 | 0.2279 | |
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| No log | 7.0 | 112 | 0.2280 | 0.3282 | 0.2280 | |
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| No log | 7.125 | 114 | 0.2237 | 0.3189 | 0.2237 | |
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| No log | 7.25 | 116 | 0.2375 | 0.2840 | 0.2375 | |
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| No log | 7.375 | 118 | 0.2582 | 0.2253 | 0.2582 | |
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| No log | 7.5 | 120 | 0.2659 | 0.1942 | 0.2659 | |
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| No log | 7.625 | 122 | 0.2631 | 0.2051 | 0.2631 | |
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| No log | 7.75 | 124 | 0.2499 | 0.2638 | 0.2499 | |
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| No log | 7.875 | 126 | 0.2375 | 0.3074 | 0.2375 | |
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| No log | 8.0 | 128 | 0.2297 | 0.3323 | 0.2297 | |
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| No log | 8.125 | 130 | 0.2295 | 0.3635 | 0.2295 | |
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| No log | 8.25 | 132 | 0.2312 | 0.3846 | 0.2312 | |
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| No log | 8.375 | 134 | 0.2318 | 0.3899 | 0.2318 | |
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| No log | 8.5 | 136 | 0.2309 | 0.3543 | 0.2309 | |
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| No log | 8.625 | 138 | 0.2285 | 0.3446 | 0.2285 | |
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| No log | 8.75 | 140 | 0.2267 | 0.3067 | 0.2267 | |
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| No log | 8.875 | 142 | 0.2250 | 0.2992 | 0.2250 | |
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| No log | 9.0 | 144 | 0.2243 | 0.2916 | 0.2243 | |
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| No log | 9.125 | 146 | 0.2236 | 0.2765 | 0.2236 | |
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| No log | 9.25 | 148 | 0.2234 | 0.2765 | 0.2234 | |
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| No log | 9.375 | 150 | 0.2237 | 0.2765 | 0.2237 | |
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| No log | 9.5 | 152 | 0.2245 | 0.2765 | 0.2245 | |
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| No log | 9.625 | 154 | 0.2251 | 0.2841 | 0.2251 | |
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| No log | 9.75 | 156 | 0.2256 | 0.2916 | 0.2256 | |
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| No log | 9.875 | 158 | 0.2256 | 0.2916 | 0.2256 | |
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| No log | 10.0 | 160 | 0.2257 | 0.2916 | 0.2257 | |
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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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