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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_organization_task1_fold2 |
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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_organization_task1_fold2 |
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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: 1.1732 |
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- Qwk: 0.1337 |
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- Mse: 1.1732 |
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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 | 0.1176 | 2 | 4.6922 | -0.0262 | 4.6922 | |
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| No log | 0.2353 | 4 | 1.9697 | -0.0193 | 1.9697 | |
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| No log | 0.3529 | 6 | 1.2474 | -0.0024 | 1.2474 | |
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| No log | 0.4706 | 8 | 1.3039 | -0.1115 | 1.3039 | |
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| No log | 0.5882 | 10 | 1.2027 | -0.0222 | 1.2027 | |
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| No log | 0.7059 | 12 | 1.1028 | 0.0290 | 1.1028 | |
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| No log | 0.8235 | 14 | 1.1603 | 0.0257 | 1.1603 | |
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| No log | 0.9412 | 16 | 1.1285 | -0.0227 | 1.1285 | |
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| No log | 1.0588 | 18 | 1.1018 | 0.0835 | 1.1018 | |
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| No log | 1.1765 | 20 | 1.1034 | -0.0341 | 1.1034 | |
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| No log | 1.2941 | 22 | 1.0966 | 0.0201 | 1.0966 | |
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| No log | 1.4118 | 24 | 1.0911 | 0.0407 | 1.0911 | |
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| No log | 1.5294 | 26 | 1.0941 | 0.0854 | 1.0941 | |
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| No log | 1.6471 | 28 | 1.1027 | 0.0379 | 1.1027 | |
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| No log | 1.7647 | 30 | 1.1067 | 0.0462 | 1.1067 | |
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| No log | 1.8824 | 32 | 1.1142 | -0.0019 | 1.1142 | |
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| No log | 2.0 | 34 | 1.1210 | 0.0205 | 1.1210 | |
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| No log | 2.1176 | 36 | 1.1209 | 0.0124 | 1.1209 | |
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| No log | 2.2353 | 38 | 1.1138 | 0.0345 | 1.1138 | |
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| No log | 2.3529 | 40 | 1.1537 | 0.0350 | 1.1537 | |
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| No log | 2.4706 | 42 | 1.1387 | 0.0164 | 1.1387 | |
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| No log | 2.5882 | 44 | 1.1467 | 0.0448 | 1.1467 | |
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| No log | 2.7059 | 46 | 1.1779 | 0.0372 | 1.1779 | |
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| No log | 2.8235 | 48 | 1.1734 | 0.0435 | 1.1734 | |
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| No log | 2.9412 | 50 | 1.0971 | 0.0562 | 1.0971 | |
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| No log | 3.0588 | 52 | 1.0868 | 0.0993 | 1.0868 | |
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| No log | 3.1765 | 54 | 1.1937 | 0.0208 | 1.1937 | |
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| No log | 3.2941 | 56 | 1.1870 | -0.0026 | 1.1870 | |
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| No log | 3.4118 | 58 | 1.1154 | 0.0605 | 1.1154 | |
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| No log | 3.5294 | 60 | 1.1425 | 0.0586 | 1.1425 | |
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| No log | 3.6471 | 62 | 1.1710 | 0.0755 | 1.1710 | |
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| No log | 3.7647 | 64 | 1.2418 | -0.0251 | 1.2418 | |
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| No log | 3.8824 | 66 | 1.2678 | -0.0157 | 1.2678 | |
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| No log | 4.0 | 68 | 1.3259 | -0.0370 | 1.3259 | |
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| No log | 4.1176 | 70 | 1.4421 | -0.0243 | 1.4421 | |
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| No log | 4.2353 | 72 | 1.3217 | -0.0265 | 1.3217 | |
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| No log | 4.3529 | 74 | 1.1731 | 0.1242 | 1.1731 | |
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| No log | 4.4706 | 76 | 1.1656 | 0.1082 | 1.1656 | |
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| No log | 4.5882 | 78 | 1.2346 | -0.0387 | 1.2346 | |
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| No log | 4.7059 | 80 | 1.3196 | 0.0306 | 1.3196 | |
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| No log | 4.8235 | 82 | 1.2508 | -0.0093 | 1.2508 | |
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| No log | 4.9412 | 84 | 1.1905 | -0.0118 | 1.1905 | |
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| No log | 5.0588 | 86 | 1.1863 | 0.0002 | 1.1863 | |
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| No log | 5.1765 | 88 | 1.2464 | 0.0063 | 1.2464 | |
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| No log | 5.2941 | 90 | 1.2248 | 0.0177 | 1.2248 | |
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| No log | 5.4118 | 92 | 1.2140 | 0.0391 | 1.2140 | |
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| No log | 5.5294 | 94 | 1.2402 | 0.0177 | 1.2402 | |
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| No log | 5.6471 | 96 | 1.1852 | 0.1192 | 1.1852 | |
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| No log | 5.7647 | 98 | 1.1973 | 0.0602 | 1.1973 | |
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| No log | 5.8824 | 100 | 1.2330 | 0.0886 | 1.2330 | |
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| No log | 6.0 | 102 | 1.1570 | 0.0504 | 1.1570 | |
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| No log | 6.1176 | 104 | 1.1259 | 0.1211 | 1.1259 | |
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| No log | 6.2353 | 106 | 1.1231 | 0.1211 | 1.1231 | |
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| No log | 6.3529 | 108 | 1.1216 | 0.0835 | 1.1216 | |
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| No log | 6.4706 | 110 | 1.1530 | 0.0907 | 1.1530 | |
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| No log | 6.5882 | 112 | 1.1410 | 0.1176 | 1.1410 | |
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| No log | 6.7059 | 114 | 1.1564 | 0.1120 | 1.1564 | |
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| No log | 6.8235 | 116 | 1.2102 | 0.0988 | 1.2102 | |
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| No log | 6.9412 | 118 | 1.2596 | 0.0681 | 1.2596 | |
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| No log | 7.0588 | 120 | 1.2375 | 0.0722 | 1.2375 | |
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| No log | 7.1765 | 122 | 1.1821 | 0.1425 | 1.1821 | |
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| No log | 7.2941 | 124 | 1.1856 | 0.1180 | 1.1856 | |
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| No log | 7.4118 | 126 | 1.1952 | 0.1542 | 1.1952 | |
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| No log | 7.5294 | 128 | 1.2010 | 0.1643 | 1.2010 | |
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| No log | 7.6471 | 130 | 1.1966 | 0.1542 | 1.1966 | |
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| No log | 7.7647 | 132 | 1.2024 | 0.1589 | 1.2024 | |
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| No log | 7.8824 | 134 | 1.2473 | 0.0820 | 1.2473 | |
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| No log | 8.0 | 136 | 1.2492 | 0.0847 | 1.2492 | |
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| No log | 8.1176 | 138 | 1.2147 | 0.1538 | 1.2147 | |
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| No log | 8.2353 | 140 | 1.1859 | 0.0946 | 1.1859 | |
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| No log | 8.3529 | 142 | 1.1767 | 0.1220 | 1.1767 | |
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| No log | 8.4706 | 144 | 1.1738 | 0.1091 | 1.1738 | |
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| No log | 8.5882 | 146 | 1.1774 | 0.1326 | 1.1774 | |
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| No log | 8.7059 | 148 | 1.1779 | 0.1337 | 1.1779 | |
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| No log | 8.8235 | 150 | 1.1724 | 0.1326 | 1.1724 | |
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| No log | 8.9412 | 152 | 1.1684 | 0.1303 | 1.1684 | |
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| No log | 9.0588 | 154 | 1.1673 | 0.1468 | 1.1673 | |
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| No log | 9.1765 | 156 | 1.1674 | 0.1468 | 1.1674 | |
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| No log | 9.2941 | 158 | 1.1677 | 0.1326 | 1.1677 | |
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| No log | 9.4118 | 160 | 1.1752 | 0.1369 | 1.1752 | |
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| No log | 9.5294 | 162 | 1.1826 | 0.1538 | 1.1826 | |
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| No log | 9.6471 | 164 | 1.1796 | 0.1229 | 1.1796 | |
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| No log | 9.7647 | 166 | 1.1767 | 0.1369 | 1.1767 | |
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| No log | 9.8824 | 168 | 1.1735 | 0.1337 | 1.1735 | |
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| No log | 10.0 | 170 | 1.1732 | 0.1337 | 1.1732 | |
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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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