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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_task7_fold5 |
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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_task7_fold5 |
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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: 0.5800 |
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- Qwk: 0.7690 |
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- Mse: 0.5800 |
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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 | 3.1878 | 0.0032 | 3.1878 | |
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| No log | 0.25 | 4 | 1.6075 | 0.1098 | 1.6075 | |
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| No log | 0.375 | 6 | 1.1035 | 0.2670 | 1.1035 | |
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| No log | 0.5 | 8 | 1.4112 | 0.3086 | 1.4112 | |
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| No log | 0.625 | 10 | 1.3436 | 0.3880 | 1.3436 | |
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| No log | 0.75 | 12 | 1.1181 | 0.3654 | 1.1181 | |
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| No log | 0.875 | 14 | 0.8279 | 0.4164 | 0.8279 | |
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| No log | 1.0 | 16 | 0.6795 | 0.4918 | 0.6795 | |
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| No log | 1.125 | 18 | 0.6997 | 0.5370 | 0.6997 | |
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| No log | 1.25 | 20 | 0.8208 | 0.6120 | 0.8208 | |
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| No log | 1.375 | 22 | 0.7785 | 0.7348 | 0.7785 | |
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| No log | 1.5 | 24 | 0.6388 | 0.7210 | 0.6388 | |
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| No log | 1.625 | 26 | 0.6688 | 0.7365 | 0.6688 | |
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| No log | 1.75 | 28 | 0.6507 | 0.7403 | 0.6507 | |
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| No log | 1.875 | 30 | 0.5320 | 0.7356 | 0.5320 | |
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| No log | 2.0 | 32 | 0.5254 | 0.7454 | 0.5254 | |
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| No log | 2.125 | 34 | 0.5348 | 0.7325 | 0.5348 | |
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| No log | 2.25 | 36 | 0.6139 | 0.7376 | 0.6139 | |
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| No log | 2.375 | 38 | 0.6648 | 0.7474 | 0.6648 | |
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| No log | 2.5 | 40 | 0.5894 | 0.7707 | 0.5894 | |
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| No log | 2.625 | 42 | 0.5580 | 0.7530 | 0.5580 | |
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| No log | 2.75 | 44 | 0.5824 | 0.7698 | 0.5824 | |
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| No log | 2.875 | 46 | 0.6444 | 0.7641 | 0.6444 | |
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| No log | 3.0 | 48 | 0.5327 | 0.7206 | 0.5327 | |
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| No log | 3.125 | 50 | 0.5871 | 0.7517 | 0.5871 | |
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| No log | 3.25 | 52 | 0.5331 | 0.7366 | 0.5331 | |
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| No log | 3.375 | 54 | 0.6130 | 0.7665 | 0.6130 | |
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| No log | 3.5 | 56 | 0.5889 | 0.7650 | 0.5889 | |
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| No log | 3.625 | 58 | 0.5848 | 0.7758 | 0.5848 | |
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| No log | 3.75 | 60 | 0.7089 | 0.7737 | 0.7089 | |
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| No log | 3.875 | 62 | 0.7846 | 0.7865 | 0.7846 | |
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| No log | 4.0 | 64 | 0.6552 | 0.7793 | 0.6552 | |
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| No log | 4.125 | 66 | 0.5020 | 0.7284 | 0.5020 | |
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| No log | 4.25 | 68 | 0.5170 | 0.7322 | 0.5170 | |
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| No log | 4.375 | 70 | 0.5877 | 0.7481 | 0.5877 | |
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| No log | 4.5 | 72 | 0.5700 | 0.7494 | 0.5700 | |
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| No log | 4.625 | 74 | 0.5147 | 0.7380 | 0.5147 | |
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| No log | 4.75 | 76 | 0.5942 | 0.7664 | 0.5942 | |
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| No log | 4.875 | 78 | 0.6564 | 0.7710 | 0.6564 | |
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| No log | 5.0 | 80 | 0.6565 | 0.7710 | 0.6565 | |
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| No log | 5.125 | 82 | 0.6572 | 0.7802 | 0.6572 | |
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| No log | 5.25 | 84 | 0.6860 | 0.7836 | 0.6860 | |
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| No log | 5.375 | 86 | 0.6265 | 0.7687 | 0.6265 | |
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| No log | 5.5 | 88 | 0.5116 | 0.7530 | 0.5116 | |
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| No log | 5.625 | 90 | 0.5026 | 0.7603 | 0.5026 | |
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| No log | 5.75 | 92 | 0.5588 | 0.7542 | 0.5588 | |
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| No log | 5.875 | 94 | 0.6752 | 0.7902 | 0.6752 | |
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| No log | 6.0 | 96 | 0.7891 | 0.7984 | 0.7891 | |
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| No log | 6.125 | 98 | 0.7038 | 0.7947 | 0.7038 | |
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| No log | 6.25 | 100 | 0.5797 | 0.7519 | 0.5797 | |
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| No log | 6.375 | 102 | 0.5895 | 0.7634 | 0.5895 | |
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| No log | 6.5 | 104 | 0.6498 | 0.7782 | 0.6498 | |
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| No log | 6.625 | 106 | 0.5864 | 0.7623 | 0.5864 | |
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| No log | 6.75 | 108 | 0.5259 | 0.7227 | 0.5259 | |
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| No log | 6.875 | 110 | 0.5133 | 0.7040 | 0.5133 | |
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| No log | 7.0 | 112 | 0.5219 | 0.7120 | 0.5219 | |
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| No log | 7.125 | 114 | 0.5822 | 0.7464 | 0.5822 | |
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| No log | 7.25 | 116 | 0.6526 | 0.7676 | 0.6526 | |
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| No log | 7.375 | 118 | 0.6628 | 0.7818 | 0.6628 | |
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| No log | 7.5 | 120 | 0.6080 | 0.7726 | 0.6080 | |
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| No log | 7.625 | 122 | 0.5645 | 0.7416 | 0.5645 | |
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| No log | 7.75 | 124 | 0.5592 | 0.7409 | 0.5592 | |
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| No log | 7.875 | 126 | 0.5637 | 0.7527 | 0.5637 | |
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| No log | 8.0 | 128 | 0.5640 | 0.7522 | 0.5640 | |
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| No log | 8.125 | 130 | 0.5743 | 0.7522 | 0.5743 | |
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| No log | 8.25 | 132 | 0.6128 | 0.7551 | 0.6128 | |
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| No log | 8.375 | 134 | 0.6083 | 0.7551 | 0.6083 | |
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| No log | 8.5 | 136 | 0.5761 | 0.7661 | 0.5761 | |
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| No log | 8.625 | 138 | 0.5522 | 0.7596 | 0.5522 | |
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| No log | 8.75 | 140 | 0.5418 | 0.7580 | 0.5418 | |
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| No log | 8.875 | 142 | 0.5541 | 0.7626 | 0.5541 | |
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| No log | 9.0 | 144 | 0.5890 | 0.7591 | 0.5890 | |
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| No log | 9.125 | 146 | 0.6347 | 0.7704 | 0.6347 | |
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| No log | 9.25 | 148 | 0.6524 | 0.7643 | 0.6524 | |
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| No log | 9.375 | 150 | 0.6441 | 0.7643 | 0.6441 | |
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| No log | 9.5 | 152 | 0.6196 | 0.7597 | 0.6196 | |
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| No log | 9.625 | 154 | 0.5988 | 0.7575 | 0.5988 | |
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| No log | 9.75 | 156 | 0.5835 | 0.7591 | 0.5835 | |
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| No log | 9.875 | 158 | 0.5803 | 0.7672 | 0.5803 | |
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| No log | 10.0 | 160 | 0.5800 | 0.7690 | 0.5800 | |
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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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