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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_task1_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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# arabert_baseline_style_task1_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: 0.5100 |
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- Qwk: 0.6698 |
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- Mse: 0.5053 |
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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 | 5.4651 | -0.0419 | 5.4241 | |
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| No log | 0.6667 | 4 | 2.3074 | 0.1693 | 2.2743 | |
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| No log | 1.0 | 6 | 1.2115 | 0.1561 | 1.2001 | |
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| No log | 1.3333 | 8 | 0.9959 | 0.4358 | 0.9923 | |
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| No log | 1.6667 | 10 | 0.7290 | 0.4921 | 0.7317 | |
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| No log | 2.0 | 12 | 0.7145 | 0.5587 | 0.7193 | |
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| No log | 2.3333 | 14 | 0.7329 | 0.5435 | 0.7351 | |
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| No log | 2.6667 | 16 | 0.7694 | 0.5219 | 0.7660 | |
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| No log | 3.0 | 18 | 0.9233 | 0.4358 | 0.9210 | |
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| No log | 3.3333 | 20 | 0.8596 | 0.4516 | 0.8545 | |
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| No log | 3.6667 | 22 | 0.7445 | 0.5743 | 0.7322 | |
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| No log | 4.0 | 24 | 0.7760 | 0.4773 | 0.7614 | |
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| No log | 4.3333 | 26 | 0.6783 | 0.5743 | 0.6684 | |
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| No log | 4.6667 | 28 | 0.7836 | 0.5152 | 0.7834 | |
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| No log | 5.0 | 30 | 0.7387 | 0.5188 | 0.7401 | |
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| No log | 5.3333 | 32 | 0.5679 | 0.5743 | 0.5656 | |
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| No log | 5.6667 | 34 | 0.5183 | 0.5743 | 0.5136 | |
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| No log | 6.0 | 36 | 0.5055 | 0.5743 | 0.5017 | |
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| No log | 6.3333 | 38 | 0.5518 | 0.5188 | 0.5511 | |
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| No log | 6.6667 | 40 | 0.6558 | 0.5188 | 0.6585 | |
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| No log | 7.0 | 42 | 0.6773 | 0.6025 | 0.6812 | |
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| No log | 7.3333 | 44 | 0.6216 | 0.6025 | 0.6242 | |
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| No log | 7.6667 | 46 | 0.5382 | 0.6698 | 0.5375 | |
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| No log | 8.0 | 48 | 0.5083 | 0.6698 | 0.5056 | |
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| No log | 8.3333 | 50 | 0.5045 | 0.6698 | 0.5004 | |
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| No log | 8.6667 | 52 | 0.5059 | 0.7151 | 0.5009 | |
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| No log | 9.0 | 54 | 0.5070 | 0.7151 | 0.5018 | |
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| No log | 9.3333 | 56 | 0.5069 | 0.7151 | 0.5016 | |
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| No log | 9.6667 | 58 | 0.5086 | 0.7151 | 0.5037 | |
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| No log | 10.0 | 60 | 0.5100 | 0.6698 | 0.5053 | |
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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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