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--- |
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base_model: SynamicTechnologies/CYBERT |
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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: Cyber-ThreaD/CyBERT-CyNER |
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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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# Cyber-ThreaD/CyBERT-CyNER |
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This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on the [CyNER](https://github.com/aiforsec/CyNER) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2405 |
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- Precision: 0.4671 |
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- Recall: 0.2810 |
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- F1: 0.3509 |
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- Accuracy: 0.9568 |
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It achieves the following results on the prediction set: |
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- Loss: 0.2747 |
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- Precision: 0.5442 |
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- Recall: 0.3483 |
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- F1: 0.4248 |
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- Accuracy: 0.9471 |
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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: 10.0 |
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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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| 0.2304 | 1.42 | 500 | 0.2405 | 0.4671 | 0.2810 | 0.3509 | 0.9568 | |
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| 0.1092 | 2.84 | 1000 | 0.2575 | 0.5426 | 0.2848 | 0.3735 | 0.9601 | |
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| 0.0797 | 4.26 | 1500 | 0.2454 | 0.4701 | 0.3308 | 0.3883 | 0.9576 | |
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| 0.0615 | 5.68 | 2000 | 0.2669 | 0.4902 | 0.3180 | 0.3857 | 0.9586 | |
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| 0.0504 | 7.1 | 2500 | 0.2687 | 0.4885 | 0.3525 | 0.4095 | 0.9580 | |
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| 0.0379 | 8.52 | 3000 | 0.2752 | 0.4656 | 0.3627 | 0.4078 | 0.9573 | |
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| 0.0339 | 9.94 | 3500 | 0.2828 | 0.4991 | 0.3499 | 0.4114 | 0.9586 | |
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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 2.14.6 |
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- Tokenizers 0.14.1 |
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### Citing & Authors |
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If you use the model kindly cite the following work |
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``` |
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@inproceedings{deka2024attacker, |
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title={AttackER: Towards Enhancing Cyber-Attack Attribution with a Named Entity Recognition Dataset}, |
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author={Deka, Pritam and Rajapaksha, Sampath and Rani, Ruby and Almutairi, Amirah and Karafili, Erisa}, |
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booktitle={International Conference on Web Information Systems Engineering}, |
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pages={255--270}, |
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year={2024}, |
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organization={Springer} |
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} |
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``` |
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