| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: fine_tuned_mix400k_arabert |
| 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. --> |
|
|
| # fine_tuned_mix400k_arabert |
| |
| 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: 0.1851 |
| - Accuracy: 0.9674 |
| - Precision: 0.9835 |
| - Recall: 0.9723 |
| - F1: 0.9778 |
| |
| ## 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: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.113 | 1.0 | 42279 | 0.1089 | 0.9541 | 0.9962 | 0.9414 | 0.9680 | |
| | 0.0957 | 2.0 | 84558 | 0.1094 | 0.9639 | 0.9869 | 0.9638 | 0.9752 | |
| | 0.0833 | 3.0 | 126837 | 0.1061 | 0.9641 | 0.9900 | 0.9611 | 0.9753 | |
| | 0.0691 | 4.0 | 169116 | 0.1558 | 0.9677 | 0.9845 | 0.9716 | 0.9780 | |
| | 0.0552 | 5.0 | 211395 | 0.1193 | 0.9676 | 0.9843 | 0.9717 | 0.9780 | |
| | 0.0411 | 6.0 | 253674 | 0.1851 | 0.9674 | 0.9835 | 0.9723 | 0.9778 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.19.2 |
| - Pytorch 1.11.0+cu113 |
| - Datasets 2.2.2 |
| - Tokenizers 0.12.1 |
| |
