| --- |
| library_name: transformers |
| base_model: aubmindlab/bert-base-arabertv02 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: fold_1 |
| 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. --> |
|
|
| # fold_1 |
| |
| This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5970 |
| - Accuracy: 0.8047 |
| - Precision: 0.8127 |
| - Recall: 0.7999 |
| - F1: 0.8023 |
| |
| ## 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: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 1.0198 | 1.1628 | 50 | 0.8447 | 0.6095 | 0.6519 | 0.6191 | 0.5959 | |
| | 0.7783 | 2.3256 | 100 | 0.7763 | 0.7101 | 0.7495 | 0.7039 | 0.7108 | |
| | 0.5724 | 3.4884 | 150 | 0.6136 | 0.7988 | 0.8080 | 0.7961 | 0.7988 | |
| | 0.4886 | 4.6512 | 200 | 0.5970 | 0.8047 | 0.8127 | 0.7999 | 0.8023 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.3 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
