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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_task1_fold4 |
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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_task1_fold4 |
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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: 0.4588 |
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- Qwk: 0.6885 |
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- Mse: 0.4588 |
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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 | 3.0429 | 0.0044 | 3.0429 | |
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| No log | 0.25 | 4 | 1.6578 | 0.1373 | 1.6578 | |
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| No log | 0.375 | 6 | 0.9445 | 0.3393 | 0.9445 | |
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| No log | 0.5 | 8 | 0.7446 | 0.4700 | 0.7446 | |
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| No log | 0.625 | 10 | 0.8109 | 0.4239 | 0.8109 | |
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| No log | 0.75 | 12 | 0.5652 | 0.6068 | 0.5652 | |
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| No log | 0.875 | 14 | 0.6877 | 0.6129 | 0.6877 | |
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| No log | 1.0 | 16 | 0.5401 | 0.6022 | 0.5401 | |
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| No log | 1.125 | 18 | 0.5571 | 0.5613 | 0.5571 | |
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| No log | 1.25 | 20 | 0.4854 | 0.6440 | 0.4854 | |
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| No log | 1.375 | 22 | 0.5443 | 0.7366 | 0.5443 | |
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| No log | 1.5 | 24 | 0.5077 | 0.7444 | 0.5077 | |
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| No log | 1.625 | 26 | 0.5015 | 0.6266 | 0.5015 | |
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| No log | 1.75 | 28 | 0.5012 | 0.6164 | 0.5012 | |
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| No log | 1.875 | 30 | 0.4504 | 0.7043 | 0.4504 | |
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| No log | 2.0 | 32 | 0.4864 | 0.7187 | 0.4864 | |
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| No log | 2.125 | 34 | 0.4305 | 0.7243 | 0.4305 | |
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| No log | 2.25 | 36 | 0.4572 | 0.6579 | 0.4572 | |
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| No log | 2.375 | 38 | 0.4545 | 0.7032 | 0.4545 | |
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| No log | 2.5 | 40 | 0.4159 | 0.7123 | 0.4159 | |
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| No log | 2.625 | 42 | 0.4122 | 0.7591 | 0.4122 | |
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| No log | 2.75 | 44 | 0.4424 | 0.7617 | 0.4424 | |
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| No log | 2.875 | 46 | 0.4110 | 0.7600 | 0.4110 | |
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| No log | 3.0 | 48 | 0.3993 | 0.7372 | 0.3993 | |
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| No log | 3.125 | 50 | 0.3990 | 0.7391 | 0.3990 | |
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| No log | 3.25 | 52 | 0.3923 | 0.7306 | 0.3923 | |
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| No log | 3.375 | 54 | 0.4375 | 0.7685 | 0.4375 | |
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| No log | 3.5 | 56 | 0.4628 | 0.7698 | 0.4628 | |
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| No log | 3.625 | 58 | 0.4089 | 0.7365 | 0.4089 | |
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| No log | 3.75 | 60 | 0.4113 | 0.7238 | 0.4113 | |
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| No log | 3.875 | 62 | 0.4117 | 0.7308 | 0.4117 | |
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| No log | 4.0 | 64 | 0.4183 | 0.7175 | 0.4183 | |
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| No log | 4.125 | 66 | 0.4326 | 0.7175 | 0.4326 | |
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| No log | 4.25 | 68 | 0.4439 | 0.7360 | 0.4439 | |
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| No log | 4.375 | 70 | 0.4530 | 0.7375 | 0.4530 | |
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| No log | 4.5 | 72 | 0.4458 | 0.7040 | 0.4458 | |
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| No log | 4.625 | 74 | 0.4431 | 0.7054 | 0.4431 | |
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| No log | 4.75 | 76 | 0.4403 | 0.6980 | 0.4403 | |
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| No log | 4.875 | 78 | 0.4350 | 0.7144 | 0.4350 | |
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| No log | 5.0 | 80 | 0.4311 | 0.7511 | 0.4311 | |
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| No log | 5.125 | 82 | 0.4257 | 0.7418 | 0.4257 | |
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| No log | 5.25 | 84 | 0.4298 | 0.7174 | 0.4298 | |
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| No log | 5.375 | 86 | 0.4420 | 0.6877 | 0.4420 | |
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| No log | 5.5 | 88 | 0.4344 | 0.7174 | 0.4344 | |
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| No log | 5.625 | 90 | 0.4324 | 0.7146 | 0.4324 | |
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| No log | 5.75 | 92 | 0.4363 | 0.7566 | 0.4363 | |
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| No log | 5.875 | 94 | 0.4499 | 0.7689 | 0.4499 | |
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| No log | 6.0 | 96 | 0.4217 | 0.7367 | 0.4217 | |
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| No log | 6.125 | 98 | 0.4252 | 0.7237 | 0.4252 | |
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| No log | 6.25 | 100 | 0.4235 | 0.7141 | 0.4235 | |
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| No log | 6.375 | 102 | 0.4211 | 0.7230 | 0.4211 | |
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| No log | 6.5 | 104 | 0.4285 | 0.7493 | 0.4285 | |
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| No log | 6.625 | 106 | 0.4367 | 0.7530 | 0.4367 | |
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| No log | 6.75 | 108 | 0.4214 | 0.7457 | 0.4214 | |
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| No log | 6.875 | 110 | 0.4380 | 0.6930 | 0.4380 | |
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| No log | 7.0 | 112 | 0.4555 | 0.6727 | 0.4555 | |
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| No log | 7.125 | 114 | 0.4358 | 0.6947 | 0.4358 | |
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| No log | 7.25 | 116 | 0.4270 | 0.7277 | 0.4270 | |
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| No log | 7.375 | 118 | 0.4349 | 0.7457 | 0.4349 | |
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| No log | 7.5 | 120 | 0.4430 | 0.7382 | 0.4430 | |
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| No log | 7.625 | 122 | 0.4539 | 0.7257 | 0.4539 | |
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| No log | 7.75 | 124 | 0.4623 | 0.7204 | 0.4623 | |
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| No log | 7.875 | 126 | 0.4640 | 0.7110 | 0.4640 | |
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| No log | 8.0 | 128 | 0.4644 | 0.7115 | 0.4644 | |
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| No log | 8.125 | 130 | 0.4639 | 0.7095 | 0.4639 | |
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| No log | 8.25 | 132 | 0.4612 | 0.7073 | 0.4612 | |
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| No log | 8.375 | 134 | 0.4652 | 0.6865 | 0.4652 | |
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| No log | 8.5 | 136 | 0.4689 | 0.6753 | 0.4689 | |
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| No log | 8.625 | 138 | 0.4608 | 0.6849 | 0.4608 | |
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| No log | 8.75 | 140 | 0.4553 | 0.6907 | 0.4553 | |
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| No log | 8.875 | 142 | 0.4538 | 0.6930 | 0.4538 | |
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| No log | 9.0 | 144 | 0.4537 | 0.7172 | 0.4537 | |
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| No log | 9.125 | 146 | 0.4564 | 0.7273 | 0.4564 | |
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| No log | 9.25 | 148 | 0.4582 | 0.7294 | 0.4582 | |
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| No log | 9.375 | 150 | 0.4572 | 0.7267 | 0.4572 | |
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| No log | 9.5 | 152 | 0.4559 | 0.7093 | 0.4559 | |
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| No log | 9.625 | 154 | 0.4566 | 0.7000 | 0.4566 | |
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| No log | 9.75 | 156 | 0.4582 | 0.6885 | 0.4582 | |
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| No log | 9.875 | 158 | 0.4588 | 0.6885 | 0.4588 | |
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| No log | 10.0 | 160 | 0.4588 | 0.6885 | 0.4588 | |
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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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