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--- |
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license: bigscience-openrail-m |
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base_model: ehsanaghaei/SecureBERT |
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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: our_data |
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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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# our_data |
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This model is a fine-tuned version of [ehsanaghaei/SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4668 |
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- Precision: 0.4762 |
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- Recall: 0.5291 |
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- F1: 0.5013 |
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- Accuracy: 0.7376 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 10.0 |
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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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| 1.9177 | 0.4 | 500 | 1.6839 | 0.06 | 0.0278 | 0.0380 | 0.6004 | |
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| 1.4976 | 0.81 | 1000 | 1.4936 | 0.2281 | 0.2659 | 0.2456 | 0.6313 | |
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| 1.2309 | 1.21 | 1500 | 1.2915 | 0.2650 | 0.3148 | 0.2878 | 0.6657 | |
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| 1.0546 | 1.61 | 2000 | 1.2454 | 0.2950 | 0.3796 | 0.3320 | 0.6804 | |
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| 0.9405 | 2.01 | 2500 | 1.2377 | 0.3613 | 0.3532 | 0.3572 | 0.6916 | |
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| 0.7501 | 2.42 | 3000 | 1.1723 | 0.3607 | 0.4180 | 0.3873 | 0.7171 | |
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| 0.7133 | 2.82 | 3500 | 1.1584 | 0.3632 | 0.4444 | 0.3998 | 0.7160 | |
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| 0.5896 | 3.22 | 4000 | 1.2288 | 0.4103 | 0.4444 | 0.4267 | 0.7306 | |
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| 0.5353 | 3.63 | 4500 | 1.2319 | 0.3978 | 0.4815 | 0.4357 | 0.7254 | |
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| 0.5432 | 4.03 | 5000 | 1.2173 | 0.4269 | 0.4868 | 0.4549 | 0.7306 | |
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| 0.4062 | 4.43 | 5500 | 1.2832 | 0.4398 | 0.5026 | 0.4691 | 0.7272 | |
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| 0.4485 | 4.83 | 6000 | 1.2196 | 0.4212 | 0.5093 | 0.4611 | 0.7412 | |
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| 0.3614 | 5.24 | 6500 | 1.3155 | 0.4325 | 0.4960 | 0.4621 | 0.7325 | |
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| 0.3308 | 5.64 | 7000 | 1.3501 | 0.4184 | 0.5119 | 0.4604 | 0.7354 | |
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| 0.3645 | 6.04 | 7500 | 1.3391 | 0.4359 | 0.5172 | 0.4731 | 0.7366 | |
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| 0.2982 | 6.45 | 8000 | 1.3889 | 0.4093 | 0.5225 | 0.4590 | 0.7315 | |
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| 0.2845 | 6.85 | 8500 | 1.4109 | 0.4452 | 0.5159 | 0.4779 | 0.7377 | |
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| 0.2482 | 7.25 | 9000 | 1.4668 | 0.4762 | 0.5291 | 0.5013 | 0.7376 | |
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| 0.2636 | 7.66 | 9500 | 1.4925 | 0.4540 | 0.5357 | 0.4915 | 0.7341 | |
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| 0.2605 | 8.06 | 10000 | 1.4916 | 0.4586 | 0.5344 | 0.4936 | 0.7405 | |
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| 0.1989 | 8.46 | 10500 | 1.5096 | 0.4661 | 0.5370 | 0.4991 | 0.7387 | |
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| 0.2415 | 8.86 | 11000 | 1.4698 | 0.4603 | 0.5450 | 0.4991 | 0.7443 | |
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| 0.2488 | 9.27 | 11500 | 1.4736 | 0.4578 | 0.5304 | 0.4914 | 0.7455 | |
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| 0.2129 | 9.67 | 12000 | 1.5067 | 0.4640 | 0.5450 | 0.5012 | 0.7439 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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