| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: fine-tuned-arabert-ned-latest |
| 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-arabert-ned-latest |
|
|
| 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: 0.1399 |
| - Accuracy: 0.9741 |
| - Precision: 0.9789 |
| - Recall: 0.9818 |
| - F1: 0.9803 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.1337 | 1.0 | 56232 | 0.1134 | 0.9716 | 0.9761 | 0.9809 | 0.9785 | |
| | 0.1031 | 2.0 | 112464 | 0.1135 | 0.9742 | 0.9765 | 0.9846 | 0.9805 | |
| | 0.0916 | 3.0 | 168696 | 0.1060 | 0.9745 | 0.9791 | 0.9822 | 0.9807 | |
| | 0.0812 | 4.0 | 224928 | 0.1138 | 0.9733 | 0.9787 | 0.9808 | 0.9798 | |
| | 0.076 | 5.0 | 281160 | 0.1277 | 0.9745 | 0.9782 | 0.9832 | 0.9807 | |
| | 0.0676 | 6.0 | 337392 | 0.1399 | 0.9741 | 0.9789 | 0.9818 | 0.9803 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.19.2 |
| - Pytorch 1.11.0+cu113 |
| - Datasets 2.2.2 |
| - Tokenizers 0.12.1 |
|
|
