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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: AraBERT_token_classification_AraEval24_18_labels_mlm1k_augmented |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# AraBERT_token_classification_AraEval24_18_labels_mlm1k_augmented |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9374 |
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- Precision: 0.0475 |
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- Recall: 0.0165 |
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- F1: 0.0245 |
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- Accuracy: 0.8620 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.5534 | 1.0 | 7396 | 0.7643 | 0.0031 | 0.0002 | 0.0003 | 0.8712 | |
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| 0.4414 | 2.0 | 14792 | 0.7713 | 0.0159 | 0.0018 | 0.0032 | 0.8638 | |
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| 0.3961 | 3.0 | 22188 | 0.7715 | 0.0137 | 0.0014 | 0.0026 | 0.8684 | |
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| 0.3484 | 4.0 | 29584 | 0.7929 | 0.0421 | 0.0065 | 0.0113 | 0.8661 | |
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| 0.3131 | 5.0 | 36980 | 0.8180 | 0.04 | 0.0107 | 0.0169 | 0.8578 | |
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| 0.2899 | 6.0 | 44376 | 0.8650 | 0.0448 | 0.0098 | 0.0161 | 0.8625 | |
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| 0.2682 | 7.0 | 51772 | 0.8725 | 0.0556 | 0.0186 | 0.0279 | 0.8551 | |
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| 0.2433 | 8.0 | 59168 | 0.8841 | 0.0521 | 0.0146 | 0.0228 | 0.8603 | |
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| 0.2384 | 9.0 | 66564 | 0.9149 | 0.0502 | 0.0155 | 0.0237 | 0.8635 | |
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| 0.2094 | 10.0 | 73960 | 0.9374 | 0.0475 | 0.0165 | 0.0245 | 0.8620 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.12.1 |
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- Datasets 2.13.2 |
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- Tokenizers 0.13.3 |
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