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---
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
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/runs/tr0iviop)
# Model4_arabertv2_base_T1_WS_A100_2nd
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: 0.1510
- F1 Micro: 0.8382
- F1 Macro: 0.7689
- Roc Auc: 0.8969
- Accuracy: 0.8024
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|
| 0.1687 | 1.0 | 507 | 0.1510 | 0.8382 | 0.7689 | 0.8969 | 0.8024 |
| 0.0892 | 2.0 | 1014 | 0.1634 | 0.8337 | 0.7637 | 0.8925 | 0.7982 |
| 0.0539 | 3.0 | 1521 | 0.1884 | 0.8318 | 0.7630 | 0.8968 | 0.7996 |
| 0.0331 | 4.0 | 2028 | 0.2067 | 0.8419 | 0.7700 | 0.9041 | 0.8108 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
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