| --- |
| library_name: transformers |
| base_model: aubmindlab/bert-base-arabertv02-twitter |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: Twitter_concatenatewithPrompt-fold4 |
| 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. --> |
|
|
| # Twitter_concatenatewithPrompt-fold4 |
| |
| 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: 0.3991 |
| - Accuracy: 0.8690 |
| - Macro F1: 0.8675 |
| - Weighted F1: 0.8688 |
| - F1 Pro: 0.9123 |
| - F1 Against: 0.8667 |
| - F1 Neutral: 0.8235 |
| |
| ## 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 | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:------:|:----------:|:----------:| |
| | 0.9325 | 1.1628 | 50 | 0.5782 | 0.7857 | 0.7852 | 0.7850 | 0.8376 | 0.7414 | 0.7767 | |
| | 0.5357 | 2.3256 | 100 | 0.4209 | 0.8333 | 0.8332 | 0.8335 | 0.8571 | 0.8226 | 0.82 | |
| | 0.3501 | 3.4884 | 150 | 0.4185 | 0.8452 | 0.8439 | 0.8449 | 0.8929 | 0.8346 | 0.8041 | |
| | 0.2385 | 4.6512 | 200 | 0.3991 | 0.8690 | 0.8675 | 0.8688 | 0.9123 | 0.8667 | 0.8235 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.6 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
