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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_development_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_development_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: 0.8675 |
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- Qwk: 0.0354 |
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- Mse: 0.8675 |
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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 | 3.8664 | -0.0149 | 3.8664 | |
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| No log | 0.2353 | 4 | 1.4090 | 0.0140 | 1.4090 | |
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| No log | 0.3529 | 6 | 0.8889 | 0.0893 | 0.8889 | |
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| No log | 0.4706 | 8 | 0.9277 | -0.1096 | 0.9277 | |
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| No log | 0.5882 | 10 | 0.9033 | -0.0849 | 0.9033 | |
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| No log | 0.7059 | 12 | 0.8647 | -0.0289 | 0.8647 | |
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| No log | 0.8235 | 14 | 0.8524 | -0.0029 | 0.8524 | |
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| No log | 0.9412 | 16 | 0.8579 | 0.0335 | 0.8579 | |
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| No log | 1.0588 | 18 | 0.8736 | 0.1138 | 0.8736 | |
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| No log | 1.1765 | 20 | 0.8546 | 0.1138 | 0.8546 | |
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| No log | 1.2941 | 22 | 0.8346 | 0.0370 | 0.8346 | |
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| No log | 1.4118 | 24 | 0.8410 | -0.0132 | 0.8410 | |
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| No log | 1.5294 | 26 | 0.8430 | 0.0871 | 0.8430 | |
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| No log | 1.6471 | 28 | 0.8280 | 0.0054 | 0.8280 | |
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| No log | 1.7647 | 30 | 0.8019 | 0.1435 | 0.8019 | |
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| No log | 1.8824 | 32 | 0.8572 | 0.0264 | 0.8572 | |
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| No log | 2.0 | 34 | 0.8264 | 0.0249 | 0.8264 | |
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| No log | 2.1176 | 36 | 0.8491 | 0.0318 | 0.8491 | |
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| No log | 2.2353 | 38 | 0.9465 | 0.0 | 0.9465 | |
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| No log | 2.3529 | 40 | 0.8744 | 0.0076 | 0.8744 | |
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| No log | 2.4706 | 42 | 0.8837 | 0.0105 | 0.8837 | |
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| No log | 2.5882 | 44 | 0.9227 | -0.0155 | 0.9227 | |
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| No log | 2.7059 | 46 | 0.8707 | -0.0166 | 0.8707 | |
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| No log | 2.8235 | 48 | 0.9522 | -0.0246 | 0.9522 | |
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| No log | 2.9412 | 50 | 1.0279 | -0.0279 | 1.0279 | |
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| No log | 3.0588 | 52 | 0.9857 | 0.0028 | 0.9857 | |
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| No log | 3.1765 | 54 | 0.8984 | 0.0071 | 0.8984 | |
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| No log | 3.2941 | 56 | 0.8708 | 0.0356 | 0.8708 | |
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| No log | 3.4118 | 58 | 0.8691 | -0.0219 | 0.8691 | |
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| No log | 3.5294 | 60 | 0.8666 | 0.0030 | 0.8666 | |
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| No log | 3.6471 | 62 | 0.8682 | -0.0216 | 0.8682 | |
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| No log | 3.7647 | 64 | 0.8560 | 0.0005 | 0.8560 | |
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| No log | 3.8824 | 66 | 0.8587 | 0.0154 | 0.8587 | |
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| No log | 4.0 | 68 | 0.8679 | 0.0575 | 0.8679 | |
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| No log | 4.1176 | 70 | 0.8602 | 0.0363 | 0.8602 | |
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| No log | 4.2353 | 72 | 0.8571 | -0.0005 | 0.8571 | |
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| No log | 4.3529 | 74 | 0.8496 | 0.0170 | 0.8496 | |
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| No log | 4.4706 | 76 | 0.8550 | 0.0797 | 0.8550 | |
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| No log | 4.5882 | 78 | 0.8587 | 0.0797 | 0.8587 | |
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| No log | 4.7059 | 80 | 0.8682 | 0.0574 | 0.8682 | |
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| No log | 4.8235 | 82 | 0.8811 | 0.0577 | 0.8811 | |
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| No log | 4.9412 | 84 | 0.8867 | 0.0577 | 0.8867 | |
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| No log | 5.0588 | 86 | 0.8858 | 0.0576 | 0.8858 | |
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| No log | 5.1765 | 88 | 0.9409 | 0.0065 | 0.9409 | |
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| No log | 5.2941 | 90 | 0.9595 | 0.0065 | 0.9595 | |
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| No log | 5.4118 | 92 | 0.9100 | 0.0576 | 0.9100 | |
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| No log | 5.5294 | 94 | 0.9069 | 0.0376 | 0.9069 | |
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| No log | 5.6471 | 96 | 0.9321 | -0.0238 | 0.9321 | |
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| No log | 5.7647 | 98 | 0.9246 | -0.0087 | 0.9246 | |
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| No log | 5.8824 | 100 | 0.9102 | 0.0061 | 0.9102 | |
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| No log | 6.0 | 102 | 0.9079 | -0.0091 | 0.9079 | |
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| No log | 6.1176 | 104 | 0.8805 | 0.0555 | 0.8805 | |
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| No log | 6.2353 | 106 | 0.8765 | 0.0356 | 0.8765 | |
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| No log | 6.3529 | 108 | 0.8826 | 0.0571 | 0.8826 | |
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| No log | 6.4706 | 110 | 0.8859 | 0.0572 | 0.8859 | |
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| No log | 6.5882 | 112 | 0.8716 | 0.0360 | 0.8716 | |
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| No log | 6.7059 | 114 | 0.8677 | 0.0363 | 0.8677 | |
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| No log | 6.8235 | 116 | 0.8685 | 0.0565 | 0.8685 | |
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| No log | 6.9412 | 118 | 0.8800 | 0.0351 | 0.8800 | |
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| No log | 7.0588 | 120 | 0.9136 | -0.0161 | 0.9136 | |
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| No log | 7.1765 | 122 | 0.9336 | 0.0076 | 0.9336 | |
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| No log | 7.2941 | 124 | 0.9134 | 0.0336 | 0.9134 | |
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| No log | 7.4118 | 126 | 0.8790 | 0.0352 | 0.8790 | |
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| No log | 7.5294 | 128 | 0.8678 | 0.0560 | 0.8678 | |
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| No log | 7.6471 | 130 | 0.8728 | 0.0731 | 0.8728 | |
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| No log | 7.7647 | 132 | 0.8699 | 0.0363 | 0.8699 | |
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| No log | 7.8824 | 134 | 0.8801 | 0.0355 | 0.8801 | |
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| No log | 8.0 | 136 | 0.9267 | 0.0082 | 0.9267 | |
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| No log | 8.1176 | 138 | 0.9701 | 0.0048 | 0.9701 | |
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| No log | 8.2353 | 140 | 1.0044 | 0.0041 | 1.0044 | |
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| No log | 8.3529 | 142 | 0.9968 | 0.0041 | 0.9968 | |
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| No log | 8.4706 | 144 | 0.9462 | -0.0196 | 0.9462 | |
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| No log | 8.5882 | 146 | 0.8966 | 0.0119 | 0.8966 | |
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| No log | 8.7059 | 148 | 0.8623 | 0.0354 | 0.8623 | |
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| No log | 8.8235 | 150 | 0.8572 | 0.1338 | 0.8572 | |
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| No log | 8.9412 | 152 | 0.8595 | 0.0913 | 0.8595 | |
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| No log | 9.0588 | 154 | 0.8634 | 0.0886 | 0.8634 | |
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| No log | 9.1765 | 156 | 0.8631 | 0.0886 | 0.8631 | |
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| No log | 9.2941 | 158 | 0.8590 | 0.1258 | 0.8590 | |
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| No log | 9.4118 | 160 | 0.8561 | 0.1130 | 0.8561 | |
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| No log | 9.5294 | 162 | 0.8560 | 0.1347 | 0.8560 | |
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| No log | 9.6471 | 164 | 0.8584 | 0.0980 | 0.8584 | |
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| No log | 9.7647 | 166 | 0.8627 | 0.0985 | 0.8627 | |
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| No log | 9.8824 | 168 | 0.8662 | 0.0354 | 0.8662 | |
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| No log | 10.0 | 170 | 0.8675 | 0.0354 | 0.8675 | |
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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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