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--- |
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library_name: transformers |
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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: Arabic_FineTuningAraBERT_AugV0_k1_task1_organization_fold1 |
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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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# Arabic_FineTuningAraBERT_AugV0_k1_task1_organization_fold1 |
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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: 0.6040 |
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- Qwk: 0.6847 |
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- Mse: 0.6040 |
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- Rmse: 0.7772 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | Rmse | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:| |
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| No log | 0.125 | 2 | 3.7867 | -0.0180 | 3.7867 | 1.9459 | |
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| No log | 0.25 | 4 | 1.9998 | 0.2320 | 1.9998 | 1.4141 | |
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| No log | 0.375 | 6 | 1.0711 | -0.0396 | 1.0711 | 1.0350 | |
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| No log | 0.5 | 8 | 0.9376 | 0.0982 | 0.9376 | 0.9683 | |
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| No log | 0.625 | 10 | 0.7792 | 0.2125 | 0.7792 | 0.8827 | |
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| No log | 0.75 | 12 | 0.5459 | 0.4731 | 0.5459 | 0.7389 | |
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| No log | 0.875 | 14 | 0.5198 | 0.51 | 0.5198 | 0.7210 | |
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| No log | 1.0 | 16 | 0.5273 | 0.5517 | 0.5273 | 0.7262 | |
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| No log | 1.125 | 18 | 0.5321 | 0.6224 | 0.5321 | 0.7295 | |
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| No log | 1.25 | 20 | 0.5241 | 0.6667 | 0.5241 | 0.7240 | |
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| No log | 1.375 | 22 | 0.4927 | 0.6486 | 0.4927 | 0.7020 | |
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| No log | 1.5 | 24 | 0.5528 | 0.5692 | 0.5528 | 0.7435 | |
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| No log | 1.625 | 26 | 0.4789 | 0.6889 | 0.4789 | 0.6920 | |
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| No log | 1.75 | 28 | 0.6811 | 0.5395 | 0.6811 | 0.8253 | |
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| No log | 1.875 | 30 | 1.0119 | 0.3772 | 1.0119 | 1.0059 | |
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| No log | 2.0 | 32 | 0.8998 | 0.3784 | 0.8998 | 0.9486 | |
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| No log | 2.125 | 34 | 0.5753 | 0.5070 | 0.5753 | 0.7585 | |
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| No log | 2.25 | 36 | 0.4745 | 0.5767 | 0.4745 | 0.6888 | |
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| No log | 2.375 | 38 | 0.5193 | 0.6147 | 0.5193 | 0.7206 | |
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| No log | 2.5 | 40 | 0.4825 | 0.5767 | 0.4825 | 0.6946 | |
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| No log | 2.625 | 42 | 0.4648 | 0.6182 | 0.4648 | 0.6818 | |
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| No log | 2.75 | 44 | 0.5393 | 0.7072 | 0.5393 | 0.7343 | |
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| No log | 2.875 | 46 | 0.5297 | 0.7220 | 0.5297 | 0.7278 | |
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| No log | 3.0 | 48 | 0.4946 | 0.7220 | 0.4946 | 0.7033 | |
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| No log | 3.125 | 50 | 0.4299 | 0.7 | 0.4299 | 0.6557 | |
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| No log | 3.25 | 52 | 0.4385 | 0.6667 | 0.4385 | 0.6622 | |
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| No log | 3.375 | 54 | 0.4424 | 0.6667 | 0.4424 | 0.6651 | |
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| No log | 3.5 | 56 | 0.4762 | 0.7 | 0.4762 | 0.6901 | |
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| No log | 3.625 | 58 | 0.5567 | 0.7 | 0.5567 | 0.7462 | |
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| No log | 3.75 | 60 | 0.5485 | 0.7 | 0.5485 | 0.7406 | |
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| No log | 3.875 | 62 | 0.5083 | 0.6290 | 0.5083 | 0.7130 | |
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| No log | 4.0 | 64 | 0.4976 | 0.6084 | 0.4976 | 0.7054 | |
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| No log | 4.125 | 66 | 0.5228 | 0.7 | 0.5228 | 0.7231 | |
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| No log | 4.25 | 68 | 0.4997 | 0.7 | 0.4997 | 0.7069 | |
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| No log | 4.375 | 70 | 0.5183 | 0.7 | 0.5183 | 0.7200 | |
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| No log | 4.5 | 72 | 0.6043 | 0.7016 | 0.6043 | 0.7774 | |
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| No log | 4.625 | 74 | 0.6707 | 0.7016 | 0.6707 | 0.8190 | |
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| No log | 4.75 | 76 | 0.6478 | 0.7016 | 0.6478 | 0.8048 | |
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| No log | 4.875 | 78 | 0.5690 | 0.6769 | 0.5690 | 0.7543 | |
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| No log | 5.0 | 80 | 0.5403 | 0.6263 | 0.5403 | 0.7350 | |
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| No log | 5.125 | 82 | 0.5245 | 0.7050 | 0.5245 | 0.7242 | |
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| No log | 5.25 | 84 | 0.5001 | 0.6978 | 0.5001 | 0.7072 | |
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| No log | 5.375 | 86 | 0.4801 | 0.6978 | 0.4801 | 0.6929 | |
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| No log | 5.5 | 88 | 0.4978 | 0.7287 | 0.4978 | 0.7055 | |
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| No log | 5.625 | 90 | 0.5501 | 0.7016 | 0.5501 | 0.7417 | |
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| No log | 5.75 | 92 | 0.6042 | 0.7219 | 0.6042 | 0.7773 | |
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| No log | 5.875 | 94 | 0.5626 | 0.7016 | 0.5626 | 0.7501 | |
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| No log | 6.0 | 96 | 0.5092 | 0.7050 | 0.5092 | 0.7136 | |
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| No log | 6.125 | 98 | 0.5001 | 0.6288 | 0.5001 | 0.7072 | |
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| No log | 6.25 | 100 | 0.4953 | 0.6288 | 0.4953 | 0.7038 | |
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| No log | 6.375 | 102 | 0.5378 | 0.7287 | 0.5378 | 0.7333 | |
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| No log | 6.5 | 104 | 0.6240 | 0.6789 | 0.6240 | 0.7899 | |
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| No log | 6.625 | 106 | 0.7492 | 0.6991 | 0.7492 | 0.8656 | |
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| No log | 6.75 | 108 | 0.8331 | 0.6415 | 0.8331 | 0.9127 | |
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| No log | 6.875 | 110 | 0.8239 | 0.6415 | 0.8239 | 0.9077 | |
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| No log | 7.0 | 112 | 0.7363 | 0.6991 | 0.7363 | 0.8581 | |
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| No log | 7.125 | 114 | 0.6138 | 0.6606 | 0.6138 | 0.7835 | |
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| No log | 7.25 | 116 | 0.5381 | 0.6573 | 0.5381 | 0.7336 | |
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| No log | 7.375 | 118 | 0.5289 | 0.6978 | 0.5289 | 0.7272 | |
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| No log | 7.5 | 120 | 0.5491 | 0.6784 | 0.5491 | 0.7410 | |
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| No log | 7.625 | 122 | 0.6250 | 0.6789 | 0.6250 | 0.7906 | |
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| No log | 7.75 | 124 | 0.7445 | 0.6991 | 0.7445 | 0.8629 | |
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| No log | 7.875 | 126 | 0.7655 | 0.6991 | 0.7655 | 0.8749 | |
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| No log | 8.0 | 128 | 0.7507 | 0.6991 | 0.7507 | 0.8664 | |
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| No log | 8.125 | 130 | 0.7196 | 0.6991 | 0.7196 | 0.8483 | |
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| No log | 8.25 | 132 | 0.6492 | 0.6975 | 0.6492 | 0.8057 | |
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| No log | 8.375 | 134 | 0.6000 | 0.6899 | 0.6000 | 0.7746 | |
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| No log | 8.5 | 136 | 0.5639 | 0.6899 | 0.5639 | 0.7509 | |
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| No log | 8.625 | 138 | 0.5275 | 0.7 | 0.5275 | 0.7263 | |
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| No log | 8.75 | 140 | 0.5147 | 0.7050 | 0.5147 | 0.7174 | |
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| No log | 8.875 | 142 | 0.5215 | 0.7050 | 0.5215 | 0.7221 | |
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| No log | 9.0 | 144 | 0.5328 | 0.6733 | 0.5328 | 0.7300 | |
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| No log | 9.125 | 146 | 0.5525 | 0.7016 | 0.5525 | 0.7433 | |
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| No log | 9.25 | 148 | 0.5649 | 0.6847 | 0.5649 | 0.7516 | |
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| No log | 9.375 | 150 | 0.5844 | 0.6847 | 0.5844 | 0.7644 | |
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| No log | 9.5 | 152 | 0.5954 | 0.6847 | 0.5954 | 0.7716 | |
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| No log | 9.625 | 154 | 0.5976 | 0.6847 | 0.5976 | 0.7730 | |
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| No log | 9.75 | 156 | 0.5980 | 0.6847 | 0.5980 | 0.7733 | |
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| No log | 9.875 | 158 | 0.6022 | 0.6847 | 0.6022 | 0.7760 | |
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| No log | 10.0 | 160 | 0.6040 | 0.6847 | 0.6040 | 0.7772 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu118 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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