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--- |
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base_model: qarib/bert-base-qarib60_860k |
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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: Qarib_arabic_keyword_extraction |
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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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# Qarib_arabic_keyword_extraction |
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This model is a fine-tuned version of [qarib/bert-base-qarib60_860k](https://huggingface.co/qarib/bert-base-qarib60_860k) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4027 |
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- Precision: 0.5369 |
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- Recall: 0.5937 |
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- F1: 0.5638 |
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- Accuracy: 0.9408 |
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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: 16 |
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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: 15 |
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- mixed_precision_training: Native AMP |
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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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| 0.2196 | 1.0 | 750 | 0.1674 | 0.4656 | 0.4190 | 0.4411 | 0.9327 | |
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| 0.1374 | 2.0 | 1500 | 0.1559 | 0.4741 | 0.5255 | 0.4985 | 0.9366 | |
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| 0.0976 | 3.0 | 2250 | 0.1711 | 0.4901 | 0.5650 | 0.5249 | 0.9378 | |
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| 0.0676 | 4.0 | 3000 | 0.1928 | 0.4884 | 0.5557 | 0.5199 | 0.9363 | |
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| 0.0474 | 5.0 | 3750 | 0.2109 | 0.5313 | 0.5438 | 0.5375 | 0.9402 | |
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| 0.0342 | 6.0 | 4500 | 0.2414 | 0.5259 | 0.5754 | 0.5495 | 0.9389 | |
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| 0.024 | 7.0 | 5250 | 0.2527 | 0.5076 | 0.5881 | 0.5449 | 0.9382 | |
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| 0.0186 | 8.0 | 6000 | 0.3029 | 0.5379 | 0.5654 | 0.5513 | 0.9400 | |
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| 0.0143 | 9.0 | 6750 | 0.3154 | 0.5307 | 0.5862 | 0.5571 | 0.9398 | |
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| 0.0108 | 10.0 | 7500 | 0.3490 | 0.5491 | 0.5810 | 0.5646 | 0.9403 | |
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| 0.0078 | 11.0 | 8250 | 0.3550 | 0.5475 | 0.5929 | 0.5693 | 0.9412 | |
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| 0.0068 | 12.0 | 9000 | 0.3681 | 0.5360 | 0.6019 | 0.5670 | 0.9406 | |
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| 0.0049 | 13.0 | 9750 | 0.3873 | 0.5264 | 0.6048 | 0.5629 | 0.9402 | |
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| 0.004 | 14.0 | 10500 | 0.3987 | 0.5380 | 0.5937 | 0.5644 | 0.9407 | |
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| 0.0034 | 15.0 | 11250 | 0.4027 | 0.5369 | 0.5937 | 0.5638 | 0.9408 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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