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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_organization_task5_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_organization_task5_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: 0.5797 |
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- Qwk: 0.6737 |
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- Mse: 0.5797 |
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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 | 1.3224 | 0.1029 | 1.3224 | |
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| No log | 0.6667 | 4 | 1.1724 | 0.0 | 1.1724 | |
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| No log | 1.0 | 6 | 1.0188 | 0.0 | 1.0188 | |
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| No log | 1.3333 | 8 | 0.9367 | 0.0 | 0.9367 | |
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| No log | 1.6667 | 10 | 0.9707 | 0.0461 | 0.9707 | |
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| No log | 2.0 | 12 | 1.0017 | 0.0940 | 1.0017 | |
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| No log | 2.3333 | 14 | 1.0101 | 0.3066 | 1.0101 | |
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| No log | 2.6667 | 16 | 0.9862 | 0.2453 | 0.9862 | |
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| No log | 3.0 | 18 | 0.9340 | 0.3962 | 0.9340 | |
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| No log | 3.3333 | 20 | 0.8741 | 0.4096 | 0.8741 | |
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| No log | 3.6667 | 22 | 0.8431 | 0.4403 | 0.8431 | |
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| No log | 4.0 | 24 | 0.7909 | 0.4898 | 0.7909 | |
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| No log | 4.3333 | 26 | 0.7355 | 0.5 | 0.7355 | |
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| No log | 4.6667 | 28 | 0.6751 | 0.6084 | 0.6751 | |
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| No log | 5.0 | 30 | 0.6350 | 0.5224 | 0.6350 | |
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| No log | 5.3333 | 32 | 0.6127 | 0.5224 | 0.6127 | |
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| No log | 5.6667 | 34 | 0.6032 | 0.5810 | 0.6032 | |
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| No log | 6.0 | 36 | 0.5973 | 0.5810 | 0.5973 | |
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| No log | 6.3333 | 38 | 0.5902 | 0.5810 | 0.5902 | |
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| No log | 6.6667 | 40 | 0.5956 | 0.7043 | 0.5956 | |
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| No log | 7.0 | 42 | 0.5976 | 0.7043 | 0.5976 | |
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| No log | 7.3333 | 44 | 0.5970 | 0.6401 | 0.5970 | |
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| No log | 7.6667 | 46 | 0.6061 | 0.6114 | 0.6061 | |
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| No log | 8.0 | 48 | 0.6064 | 0.6114 | 0.6064 | |
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| No log | 8.3333 | 50 | 0.5917 | 0.6401 | 0.5917 | |
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| No log | 8.6667 | 52 | 0.5644 | 0.6737 | 0.5644 | |
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| No log | 9.0 | 54 | 0.5637 | 0.6737 | 0.5637 | |
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| No log | 9.3333 | 56 | 0.5746 | 0.6737 | 0.5746 | |
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| No log | 9.6667 | 58 | 0.5782 | 0.6737 | 0.5782 | |
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| No log | 10.0 | 60 | 0.5797 | 0.6737 | 0.5797 | |
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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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