---
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
  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-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

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: 0.0602
- F1-micro: 0.8238
- Roc Auc: 0.8998
- Accuracy: 0.7891

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:--------:|
| 0.0282        | 1.0   | 507  | 0.0602          | 0.8238   | 0.8998  | 0.7891   |
| 0.0169        | 2.0   | 1014 | 0.0640          | 0.8215   | 0.8973  | 0.7765   |
| 0.0163        | 3.0   | 1521 | 0.0669          | 0.8178   | 0.8949  | 0.7716   |
| 0.0102        | 4.0   | 2028 | 0.0731          | 0.8190   | 0.9019  | 0.7877   |


### Framework versions

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