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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_baseline_style_task7_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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# arabert_baseline_style_task7_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: 0.3701 |
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- Qwk: 0.7283 |
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- Mse: 0.3603 |
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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: 16 |
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- eval_batch_size: 16 |
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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 | 0.3333 | 2 | 0.7720 | 0.3388 | 0.7758 | |
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| No log | 0.6667 | 4 | 0.5488 | 0.6361 | 0.5756 | |
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| No log | 1.0 | 6 | 0.6199 | 0.6083 | 0.6502 | |
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| No log | 1.3333 | 8 | 0.5891 | 0.3780 | 0.5962 | |
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| No log | 1.6667 | 10 | 0.5013 | 0.5556 | 0.5111 | |
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| No log | 2.0 | 12 | 0.4841 | 0.7640 | 0.4991 | |
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| No log | 2.3333 | 14 | 0.4386 | 0.784 | 0.4514 | |
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| No log | 2.6667 | 16 | 0.4343 | 0.6667 | 0.4393 | |
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| No log | 3.0 | 18 | 0.4876 | 0.6206 | 0.4868 | |
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| No log | 3.3333 | 20 | 0.4572 | 0.6294 | 0.4528 | |
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| No log | 3.6667 | 22 | 0.3651 | 0.6769 | 0.3604 | |
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| No log | 4.0 | 24 | 0.3743 | 0.6434 | 0.3662 | |
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| No log | 4.3333 | 26 | 0.4705 | 0.6261 | 0.4557 | |
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| No log | 4.6667 | 28 | 0.5940 | 0.5033 | 0.5754 | |
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| No log | 5.0 | 30 | 0.5564 | 0.5650 | 0.5374 | |
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| No log | 5.3333 | 32 | 0.4207 | 0.6607 | 0.4059 | |
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| No log | 5.6667 | 34 | 0.3696 | 0.6637 | 0.3577 | |
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| No log | 6.0 | 36 | 0.3816 | 0.6607 | 0.3687 | |
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| No log | 6.3333 | 38 | 0.4231 | 0.6261 | 0.4080 | |
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| No log | 6.6667 | 40 | 0.4191 | 0.6261 | 0.4044 | |
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| No log | 7.0 | 42 | 0.4158 | 0.6261 | 0.4016 | |
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| No log | 7.3333 | 44 | 0.4009 | 0.6261 | 0.3875 | |
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| No log | 7.6667 | 46 | 0.3811 | 0.6474 | 0.3694 | |
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| No log | 8.0 | 48 | 0.3746 | 0.6810 | 0.3639 | |
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| No log | 8.3333 | 50 | 0.3601 | 0.7283 | 0.3514 | |
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| No log | 8.6667 | 52 | 0.3557 | 0.7283 | 0.3474 | |
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| No log | 9.0 | 54 | 0.3591 | 0.7283 | 0.3506 | |
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| No log | 9.3333 | 56 | 0.3648 | 0.7283 | 0.3558 | |
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| No log | 9.6667 | 58 | 0.3690 | 0.7283 | 0.3593 | |
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| No log | 10.0 | 60 | 0.3701 | 0.7283 | 0.3603 | |
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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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