---
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__BL
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<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-BL/runs/o8owefo8)
[<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-BL/runs/o8owefo8)
# Model4_withclasess-arabertv2_base_T2_WS_A100v2_F1__BL

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0770
- F1-micro: 0.8282
- Roc Auc: 0.9072
- Accuracy: 0.7912

## 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 OptimizerNames.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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1-micro | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:--------:|
| 0.1028        | 1.0   | 507  | 0.0644          | 0.7904   | 0.8639  | 0.7297   |
| 0.0476        | 2.0   | 1014 | 0.0556          | 0.8143   | 0.8828  | 0.7668   |
| 0.0308        | 3.0   | 1521 | 0.0570          | 0.8200   | 0.8929  | 0.7758   |
| 0.0206        | 4.0   | 2028 | 0.0624          | 0.8179   | 0.8979  | 0.7828   |
| 0.0134        | 5.0   | 2535 | 0.0696          | 0.8183   | 0.9016  | 0.7856   |
| 0.0097        | 6.0   | 3042 | 0.0743          | 0.8226   | 0.9052  | 0.7898   |
| 0.0077        | 7.0   | 3549 | 0.0779          | 0.8166   | 0.9039  | 0.7793   |
| 0.0054        | 8.0   | 4056 | 0.0809          | 0.8249   | 0.9063  | 0.7905   |
| 0.0045        | 9.0   | 4563 | 0.0770          | 0.8282   | 0.9072  | 0.7912   |
| 0.0036        | 10.0  | 5070 | 0.0812          | 0.8228   | 0.9049  | 0.7849   |
| 0.003         | 11.0  | 5577 | 0.0874          | 0.8250   | 0.9072  | 0.7919   |
| 0.0025        | 12.0  | 6084 | 0.0886          | 0.8258   | 0.9067  | 0.7863   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0