| | --- |
| | library_name: transformers |
| | base_model: aubmindlab/bert-base-arabertv2 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: bert-base-arabertv2_D3Lex_CE_19levels |
| | 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. --> |
| |
|
| | # bert-base-arabertv2_D3Lex_CE_19levels |
| | |
| | This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.8342 |
| | - Macro F1: 0.4364 |
| | - Macro Precision: 0.4424 |
| | - Macro Recall: 0.4454 |
| | - Accuracy: 0.5140 |
| | - Accuracy With Margin: 0.6744 |
| | - Distance: 1.2527 |
| | - Quadratic weighted kappa: 0.7889 |
| | - Accuracy 7: 0.6179 |
| | - Accuracy 5: 0.6653 |
| | - Accuracy 3: 0.7363 |
| | |
| | ## 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 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 64 |
| | - 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: 6 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Macro Precision | Macro Recall | Accuracy | Accuracy With Margin | Distance | Quadratic weighted kappa | Accuracy 7 | Accuracy 5 | Accuracy 3 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:--------------------:|:--------:|:------------------------:|:----------:|:----------:|:----------:| |
| | | 1.8254 | 1.0 | 857 | 1.4573 | 0.3478 | 0.3741 | 0.3804 | 0.5044 | 0.6513 | 1.3171 | 0.7790 | 0.6223 | 0.6773 | 0.7415 | |
| | | 1.2699 | 2.0 | 1714 | 1.4409 | 0.4133 | 0.4330 | 0.4316 | 0.5157 | 0.6688 | 1.2666 | 0.7825 | 0.6230 | 0.6802 | 0.7497 | |
| | | 1.0213 | 3.0 | 2571 | 1.4714 | 0.4157 | 0.4210 | 0.4276 | 0.5272 | 0.6711 | 1.2473 | 0.7889 | 0.6308 | 0.6780 | 0.7458 | |
| | | 0.8308 | 4.0 | 3428 | 1.6169 | 0.4074 | 0.4422 | 0.4110 | 0.5171 | 0.6724 | 1.2573 | 0.7848 | 0.6194 | 0.6663 | 0.7397 | |
| | | 0.6128 | 5.0 | 4285 | 1.7657 | 0.4222 | 0.4375 | 0.4349 | 0.5164 | 0.6737 | 1.2575 | 0.7861 | 0.6208 | 0.6703 | 0.7391 | |
| | | 0.4786 | 6.0 | 5142 | 1.8342 | 0.4364 | 0.4424 | 0.4454 | 0.5140 | 0.6744 | 1.2527 | 0.7889 | 0.6179 | 0.6653 | 0.7363 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.53.2 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.21.2 |
| |
|
