---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02-twitter
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Model4_withclasess-arabertv2_base_T2_WS_A100v2_F1
  results: []
---

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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-T2-ArabertTv2-Bas-WS-A100/runs/ewc3vyii)
[<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-T2-ArabertTv2-Bas-WS-A100/runs/ewc3vyii)
[<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-T2-ArabertTv2-Bas-WS-A100/runs/ewc3vyii)
[<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-T2-ArabertTv2-Bas-WS-A100/runs/ewc3vyii)
# Model4_withclasess-arabertv2_base_T2_WS_A100v2_F1

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.
It achieves the following results on the evaluation set:
- Loss: 0.0813
- F1-micro: 0.8359
- Roc Auc: 0.9123
- Accuracy: 0.7975

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1-micro | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:--------:|
| 0.0095        | 1.0   | 507  | 0.0767          | 0.8272   | 0.9095  | 0.7863   |
| 0.0108        | 2.0   | 1014 | 0.0763          | 0.8237   | 0.9074  | 0.7842   |
| 0.0108        | 3.0   | 1521 | 0.0749          | 0.8199   | 0.9029  | 0.7793   |
| 0.0069        | 4.0   | 2028 | 0.0841          | 0.8299   | 0.9076  | 0.7961   |
| 0.0057        | 5.0   | 2535 | 0.0835          | 0.8286   | 0.9105  | 0.7947   |
| 0.0037        | 6.0   | 3042 | 0.0813          | 0.8359   | 0.9123  | 0.7975   |
| 0.0029        | 7.0   | 3549 | 0.0875          | 0.8240   | 0.9081  | 0.7828   |
| 0.0023        | 8.0   | 4056 | 0.0928          | 0.8334   | 0.9136  | 0.8010   |
| 0.002         | 9.0   | 4563 | 0.0961          | 0.8159   | 0.9063  | 0.7730   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3