---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02-twitter
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Model4_arabertv2_base_T1_WS_A100_2nd_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-T1-ArabertTv2-Bas-WS-A100/runs/tr0iviop)
[<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)
[<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)
# Model4_arabertv2_base_T1_WS_A100_2nd_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: 0.2587
- F1 Micro: 0.8495
- F1 Macro: 0.7753
- Roc Auc: 0.9091
- Accuracy: 0.8254

## 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 | F1 Macro | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|
| 0.0226        | 1.0   | 507  | 0.2380          | 0.8341   | 0.7767   | 0.9012  | 0.8094   |
| 0.0303        | 2.0   | 1014 | 0.2433          | 0.8341   | 0.7606   | 0.8980  | 0.8094   |
| 0.0179        | 3.0   | 1521 | 0.2635          | 0.8308   | 0.7540   | 0.8987  | 0.8024   |
| 0.0141        | 4.0   | 2028 | 0.2587          | 0.8495   | 0.7753   | 0.9091  | 0.8254   |
| 0.0083        | 5.0   | 2535 | 0.2919          | 0.8353   | 0.7731   | 0.9017  | 0.8059   |
| 0.0066        | 6.0   | 3042 | 0.2922          | 0.8329   | 0.7611   | 0.9006  | 0.8045   |
| 0.0054        | 7.0   | 3549 | 0.3193          | 0.8358   | 0.7752   | 0.9016  | 0.8128   |


### Framework versions

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