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--- |
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library_name: transformers |
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base_model: aubmindlab/bert-base-arabertv02-twitter |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: Model4_arabertv2_base_T1_WS_A100_2nd_F1 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/so/Model4-with-add-clasess-T1-ArabertTv2-Bas-WS-A100/runs/tr0iviop) |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/so/Model4-with-add-clasess-T1-ArabertTv2-Bas-WS-A100/runs/tr0iviop) |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/so/Model4-with-add-clasess-T1-ArabertTv2-Bas-WS-A100/runs/tr0iviop) |
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# Model4_arabertv2_base_T1_WS_A100_2nd_F1 |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2587 |
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- F1 Micro: 0.8495 |
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- F1 Macro: 0.7753 |
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- Roc Auc: 0.9091 |
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- Accuracy: 0.8254 |
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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: 5e-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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:| |
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| 0.0226 | 1.0 | 507 | 0.2380 | 0.8341 | 0.7767 | 0.9012 | 0.8094 | |
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| 0.0303 | 2.0 | 1014 | 0.2433 | 0.8341 | 0.7606 | 0.8980 | 0.8094 | |
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| 0.0179 | 3.0 | 1521 | 0.2635 | 0.8308 | 0.7540 | 0.8987 | 0.8024 | |
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| 0.0141 | 4.0 | 2028 | 0.2587 | 0.8495 | 0.7753 | 0.9091 | 0.8254 | |
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| 0.0083 | 5.0 | 2535 | 0.2919 | 0.8353 | 0.7731 | 0.9017 | 0.8059 | |
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| 0.0066 | 6.0 | 3042 | 0.2922 | 0.8329 | 0.7611 | 0.9006 | 0.8045 | |
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| 0.0054 | 7.0 | 3549 | 0.3193 | 0.8358 | 0.7752 | 0.9016 | 0.8128 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.20.3 |
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