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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_fold0 |
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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_fold0 |
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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: 0.9449 |
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- Qwk: 0.5896 |
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- Mse: 0.9449 |
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- Rmse: 0.9721 |
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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 | 0.1176 | 2 | 5.1330 | 0.0904 | 5.1330 | 2.2656 | |
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| No log | 0.2353 | 4 | 3.4675 | 0.0 | 3.4675 | 1.8621 | |
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| No log | 0.3529 | 6 | 2.3648 | 0.0696 | 2.3648 | 1.5378 | |
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| No log | 0.4706 | 8 | 1.6993 | 0.0 | 1.6993 | 1.3036 | |
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| No log | 0.5882 | 10 | 1.4000 | 0.1973 | 1.4000 | 1.1832 | |
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| No log | 0.7059 | 12 | 1.3257 | 0.1933 | 1.3257 | 1.1514 | |
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| No log | 0.8235 | 14 | 1.2645 | 0.2939 | 1.2645 | 1.1245 | |
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| No log | 0.9412 | 16 | 1.2126 | 0.2668 | 1.2126 | 1.1012 | |
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| No log | 1.0588 | 18 | 0.9516 | 0.3259 | 0.9516 | 0.9755 | |
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| No log | 1.1765 | 20 | 0.8599 | 0.4590 | 0.8599 | 0.9273 | |
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| No log | 1.2941 | 22 | 0.8162 | 0.4085 | 0.8162 | 0.9035 | |
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| No log | 1.4118 | 24 | 0.9496 | 0.3478 | 0.9496 | 0.9745 | |
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| No log | 1.5294 | 26 | 1.1079 | 0.3209 | 1.1079 | 1.0526 | |
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| No log | 1.6471 | 28 | 1.1311 | 0.3209 | 1.1311 | 1.0635 | |
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| No log | 1.7647 | 30 | 1.0768 | 0.3494 | 1.0768 | 1.0377 | |
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| No log | 1.8824 | 32 | 1.0150 | 0.5698 | 1.0150 | 1.0075 | |
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| No log | 2.0 | 34 | 1.0474 | 0.5468 | 1.0474 | 1.0234 | |
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| No log | 2.1176 | 36 | 0.9894 | 0.5653 | 0.9894 | 0.9947 | |
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| No log | 2.2353 | 38 | 0.8745 | 0.5830 | 0.8745 | 0.9351 | |
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| No log | 2.3529 | 40 | 0.8798 | 0.6738 | 0.8798 | 0.9380 | |
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| No log | 2.4706 | 42 | 1.0482 | 0.5830 | 1.0482 | 1.0238 | |
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| No log | 2.5882 | 44 | 1.1713 | 0.5660 | 1.1713 | 1.0823 | |
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| No log | 2.7059 | 46 | 1.0797 | 0.5686 | 1.0797 | 1.0391 | |
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| No log | 2.8235 | 48 | 0.9651 | 0.5686 | 0.9651 | 0.9824 | |
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| No log | 2.9412 | 50 | 0.8528 | 0.6338 | 0.8528 | 0.9235 | |
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| No log | 3.0588 | 52 | 0.8694 | 0.5312 | 0.8694 | 0.9324 | |
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| No log | 3.1765 | 54 | 0.9500 | 0.5312 | 0.9500 | 0.9747 | |
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| No log | 3.2941 | 56 | 0.9084 | 0.5312 | 0.9084 | 0.9531 | |
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| No log | 3.4118 | 58 | 0.8059 | 0.5312 | 0.8059 | 0.8977 | |
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| No log | 3.5294 | 60 | 0.8138 | 0.6338 | 0.8138 | 0.9021 | |
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| No log | 3.6471 | 62 | 0.9146 | 0.5422 | 0.9146 | 0.9564 | |
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| No log | 3.7647 | 64 | 0.9550 | 0.5365 | 0.9550 | 0.9773 | |
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| No log | 3.8824 | 66 | 0.9336 | 0.5365 | 0.9336 | 0.9662 | |
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| No log | 4.0 | 68 | 0.9319 | 0.5625 | 0.9319 | 0.9654 | |
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| No log | 4.1176 | 70 | 0.8851 | 0.6260 | 0.8851 | 0.9408 | |
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| No log | 4.2353 | 72 | 0.8516 | 0.6182 | 0.8516 | 0.9228 | |
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| No log | 4.3529 | 74 | 0.8250 | 0.6188 | 0.8250 | 0.9083 | |
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| No log | 4.4706 | 76 | 0.8158 | 0.6690 | 0.8158 | 0.9032 | |
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| No log | 4.5882 | 78 | 0.8102 | 0.5882 | 0.8102 | 0.9001 | |
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| No log | 4.7059 | 80 | 0.8191 | 0.5882 | 0.8191 | 0.9051 | |
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| No log | 4.8235 | 82 | 0.8353 | 0.6441 | 0.8353 | 0.9139 | |
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| No log | 4.9412 | 84 | 0.8613 | 0.6213 | 0.8613 | 0.9281 | |
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| No log | 5.0588 | 86 | 0.9234 | 0.6038 | 0.9234 | 0.9610 | |
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| No log | 5.1765 | 88 | 0.9213 | 0.5714 | 0.9213 | 0.9598 | |
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| No log | 5.2941 | 90 | 0.8488 | 0.5563 | 0.8488 | 0.9213 | |
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| No log | 5.4118 | 92 | 0.8263 | 0.5563 | 0.8263 | 0.9090 | |
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| No log | 5.5294 | 94 | 0.8231 | 0.5896 | 0.8231 | 0.9073 | |
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| No log | 5.6471 | 96 | 0.8229 | 0.6213 | 0.8229 | 0.9071 | |
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| No log | 5.7647 | 98 | 0.8101 | 0.6213 | 0.8101 | 0.9000 | |
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| No log | 5.8824 | 100 | 0.7902 | 0.6732 | 0.7902 | 0.8889 | |
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| No log | 6.0 | 102 | 0.7975 | 0.6732 | 0.7975 | 0.8930 | |
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| No log | 6.1176 | 104 | 0.7976 | 0.6732 | 0.7976 | 0.8931 | |
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| No log | 6.2353 | 106 | 0.8341 | 0.6732 | 0.8341 | 0.9133 | |
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| No log | 6.3529 | 108 | 0.8567 | 0.6677 | 0.8567 | 0.9256 | |
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| No log | 6.4706 | 110 | 0.9204 | 0.6213 | 0.9204 | 0.9594 | |
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| No log | 6.5882 | 112 | 0.9584 | 0.6038 | 0.9584 | 0.9790 | |
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| No log | 6.7059 | 114 | 0.9535 | 0.6213 | 0.9535 | 0.9765 | |
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| No log | 6.8235 | 116 | 0.9361 | 0.6213 | 0.9361 | 0.9675 | |
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| No log | 6.9412 | 118 | 0.8850 | 0.6213 | 0.8850 | 0.9407 | |
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| No log | 7.0588 | 120 | 0.8731 | 0.6213 | 0.8731 | 0.9344 | |
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| No log | 7.1765 | 122 | 0.8714 | 0.6213 | 0.8714 | 0.9335 | |
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| No log | 7.2941 | 124 | 0.8738 | 0.6213 | 0.8738 | 0.9348 | |
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| No log | 7.4118 | 126 | 0.8868 | 0.6213 | 0.8868 | 0.9417 | |
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| No log | 7.5294 | 128 | 0.9237 | 0.6213 | 0.9237 | 0.9611 | |
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| No log | 7.6471 | 130 | 0.9781 | 0.5714 | 0.9781 | 0.9890 | |
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| No log | 7.7647 | 132 | 0.9926 | 0.5686 | 0.9926 | 0.9963 | |
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| No log | 7.8824 | 134 | 0.9841 | 0.5686 | 0.9841 | 0.9920 | |
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| No log | 8.0 | 136 | 0.9469 | 0.5896 | 0.9469 | 0.9731 | |
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| No log | 8.1176 | 138 | 0.9149 | 0.5896 | 0.9149 | 0.9565 | |
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| No log | 8.2353 | 140 | 0.8707 | 0.6677 | 0.8707 | 0.9331 | |
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| No log | 8.3529 | 142 | 0.8480 | 0.7178 | 0.8480 | 0.9209 | |
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| No log | 8.4706 | 144 | 0.8500 | 0.6677 | 0.8500 | 0.9219 | |
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| No log | 8.5882 | 146 | 0.8616 | 0.6677 | 0.8616 | 0.9282 | |
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| No log | 8.7059 | 148 | 0.8742 | 0.6677 | 0.8742 | 0.9350 | |
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| No log | 8.8235 | 150 | 0.8966 | 0.5896 | 0.8966 | 0.9469 | |
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| No log | 8.9412 | 152 | 0.9197 | 0.5896 | 0.9197 | 0.9590 | |
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| No log | 9.0588 | 154 | 0.9430 | 0.5896 | 0.9430 | 0.9711 | |
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| No log | 9.1765 | 156 | 0.9552 | 0.5714 | 0.9552 | 0.9774 | |
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| No log | 9.2941 | 158 | 0.9605 | 0.5714 | 0.9605 | 0.9800 | |
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| No log | 9.4118 | 160 | 0.9609 | 0.5714 | 0.9609 | 0.9803 | |
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| No log | 9.5294 | 162 | 0.9624 | 0.5714 | 0.9624 | 0.9810 | |
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| No log | 9.6471 | 164 | 0.9603 | 0.5896 | 0.9603 | 0.9799 | |
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| No log | 9.7647 | 166 | 0.9534 | 0.5896 | 0.9534 | 0.9764 | |
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| No log | 9.8824 | 168 | 0.9476 | 0.5896 | 0.9476 | 0.9735 | |
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| No log | 10.0 | 170 | 0.9449 | 0.5896 | 0.9449 | 0.9721 | |
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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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