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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_task6_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_organization_task6_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: 0.6971 |
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- Qwk: 0.5467 |
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- Mse: 0.6953 |
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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 | 2.1812 | 0.0813 | 2.1796 | |
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| No log | 0.2353 | 4 | 1.1581 | 0.1541 | 1.1560 | |
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| No log | 0.3529 | 6 | 0.9175 | 0.4300 | 0.9172 | |
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| No log | 0.4706 | 8 | 0.7853 | 0.5387 | 0.7849 | |
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| No log | 0.5882 | 10 | 0.7421 | 0.3500 | 0.7415 | |
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| No log | 0.7059 | 12 | 0.7475 | 0.3575 | 0.7469 | |
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| No log | 0.8235 | 14 | 0.5277 | 0.6055 | 0.5272 | |
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| No log | 0.9412 | 16 | 0.6059 | 0.7252 | 0.6066 | |
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| No log | 1.0588 | 18 | 0.5616 | 0.7136 | 0.5620 | |
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| No log | 1.1765 | 20 | 0.5211 | 0.6304 | 0.5204 | |
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| No log | 1.2941 | 22 | 0.6860 | 0.5190 | 0.6841 | |
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| No log | 1.4118 | 24 | 0.6738 | 0.5216 | 0.6721 | |
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| No log | 1.5294 | 26 | 0.5480 | 0.6313 | 0.5472 | |
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| No log | 1.6471 | 28 | 0.5424 | 0.7060 | 0.5428 | |
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| No log | 1.7647 | 30 | 0.4918 | 0.6873 | 0.4920 | |
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| No log | 1.8824 | 32 | 0.5098 | 0.5684 | 0.5094 | |
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| No log | 2.0 | 34 | 0.5437 | 0.5294 | 0.5430 | |
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| No log | 2.1176 | 36 | 0.5312 | 0.5901 | 0.5302 | |
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| No log | 2.2353 | 38 | 0.5616 | 0.5966 | 0.5604 | |
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| No log | 2.3529 | 40 | 0.5882 | 0.5838 | 0.5868 | |
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| No log | 2.4706 | 42 | 0.5423 | 0.6000 | 0.5413 | |
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| No log | 2.5882 | 44 | 0.5067 | 0.6211 | 0.5059 | |
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| No log | 2.7059 | 46 | 0.4934 | 0.6349 | 0.4926 | |
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| No log | 2.8235 | 48 | 0.4940 | 0.6329 | 0.4932 | |
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| No log | 2.9412 | 50 | 0.5291 | 0.5677 | 0.5279 | |
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| No log | 3.0588 | 52 | 0.6166 | 0.5158 | 0.6151 | |
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| No log | 3.1765 | 54 | 0.6014 | 0.5588 | 0.5998 | |
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| No log | 3.2941 | 56 | 0.5316 | 0.5878 | 0.5303 | |
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| No log | 3.4118 | 58 | 0.5135 | 0.5990 | 0.5124 | |
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| No log | 3.5294 | 60 | 0.5285 | 0.5827 | 0.5273 | |
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| No log | 3.6471 | 62 | 0.5943 | 0.5492 | 0.5929 | |
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| No log | 3.7647 | 64 | 0.5882 | 0.5620 | 0.5868 | |
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| No log | 3.8824 | 66 | 0.5237 | 0.5937 | 0.5227 | |
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| No log | 4.0 | 68 | 0.5270 | 0.6150 | 0.5261 | |
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| No log | 4.1176 | 70 | 0.5820 | 0.5589 | 0.5806 | |
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| No log | 4.2353 | 72 | 0.6445 | 0.5284 | 0.6429 | |
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| No log | 4.3529 | 74 | 0.6153 | 0.5627 | 0.6139 | |
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| No log | 4.4706 | 76 | 0.6066 | 0.5783 | 0.6054 | |
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| No log | 4.5882 | 78 | 0.6378 | 0.5639 | 0.6363 | |
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| No log | 4.7059 | 80 | 0.7155 | 0.5342 | 0.7135 | |
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| No log | 4.8235 | 82 | 0.7123 | 0.5305 | 0.7104 | |
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| No log | 4.9412 | 84 | 0.6786 | 0.5363 | 0.6769 | |
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| No log | 5.0588 | 86 | 0.6340 | 0.5611 | 0.6326 | |
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| No log | 5.1765 | 88 | 0.6050 | 0.5630 | 0.6038 | |
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| No log | 5.2941 | 90 | 0.6307 | 0.5564 | 0.6293 | |
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| No log | 5.4118 | 92 | 0.6603 | 0.5449 | 0.6588 | |
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| No log | 5.5294 | 94 | 0.6765 | 0.5483 | 0.6748 | |
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| No log | 5.6471 | 96 | 0.6364 | 0.5686 | 0.6351 | |
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| No log | 5.7647 | 98 | 0.6144 | 0.5967 | 0.6132 | |
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| No log | 5.8824 | 100 | 0.6315 | 0.5826 | 0.6300 | |
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| No log | 6.0 | 102 | 0.6964 | 0.5217 | 0.6946 | |
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| No log | 6.1176 | 104 | 0.6906 | 0.5310 | 0.6887 | |
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| No log | 6.2353 | 106 | 0.6656 | 0.5513 | 0.6639 | |
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| No log | 6.3529 | 108 | 0.6273 | 0.5829 | 0.6259 | |
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| No log | 6.4706 | 110 | 0.6354 | 0.5748 | 0.6340 | |
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| No log | 6.5882 | 112 | 0.6855 | 0.5397 | 0.6839 | |
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| No log | 6.7059 | 114 | 0.7228 | 0.5179 | 0.7211 | |
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| No log | 6.8235 | 116 | 0.6976 | 0.5206 | 0.6960 | |
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| No log | 6.9412 | 118 | 0.6558 | 0.5456 | 0.6544 | |
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| No log | 7.0588 | 120 | 0.6618 | 0.5569 | 0.6605 | |
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| No log | 7.1765 | 122 | 0.7088 | 0.5397 | 0.7072 | |
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| No log | 7.2941 | 124 | 0.8015 | 0.4900 | 0.7996 | |
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| No log | 7.4118 | 126 | 0.8354 | 0.4798 | 0.8334 | |
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| No log | 7.5294 | 128 | 0.7861 | 0.4973 | 0.7842 | |
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| No log | 7.6471 | 130 | 0.7081 | 0.5399 | 0.7065 | |
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| No log | 7.7647 | 132 | 0.6756 | 0.5725 | 0.6741 | |
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| No log | 7.8824 | 134 | 0.6874 | 0.5524 | 0.6859 | |
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| No log | 8.0 | 136 | 0.7225 | 0.5459 | 0.7207 | |
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| No log | 8.1176 | 138 | 0.7336 | 0.5368 | 0.7317 | |
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| No log | 8.2353 | 140 | 0.7330 | 0.5258 | 0.7312 | |
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| No log | 8.3529 | 142 | 0.7088 | 0.5474 | 0.7070 | |
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| No log | 8.4706 | 144 | 0.7009 | 0.5474 | 0.6991 | |
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| No log | 8.5882 | 146 | 0.6880 | 0.5489 | 0.6863 | |
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| No log | 8.7059 | 148 | 0.6695 | 0.5530 | 0.6679 | |
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| No log | 8.8235 | 150 | 0.6654 | 0.5587 | 0.6638 | |
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| No log | 8.9412 | 152 | 0.6764 | 0.5464 | 0.6747 | |
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| No log | 9.0588 | 154 | 0.6945 | 0.5419 | 0.6927 | |
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| No log | 9.1765 | 156 | 0.7081 | 0.5278 | 0.7063 | |
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| No log | 9.2941 | 158 | 0.7099 | 0.5292 | 0.7080 | |
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| No log | 9.4118 | 160 | 0.7043 | 0.5419 | 0.7025 | |
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| No log | 9.5294 | 162 | 0.7049 | 0.5430 | 0.7031 | |
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| No log | 9.6471 | 164 | 0.7019 | 0.5430 | 0.7000 | |
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| No log | 9.7647 | 166 | 0.6996 | 0.5467 | 0.6978 | |
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| No log | 9.8824 | 168 | 0.6977 | 0.5467 | 0.6959 | |
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| No log | 10.0 | 170 | 0.6971 | 0.5467 | 0.6953 | |
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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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