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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-APTNER |
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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-APTNER |
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This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on the [APTNER](https://github.com/wangxuren/APTNER) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4543 |
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- Precision: 0.4372 |
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- Recall: 0.4293 |
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- F1: 0.4332 |
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- Accuracy: 0.9043 |
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It achieves the following results on the prediction set: |
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- Loss: 0.3602 |
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- Precision: 0.5489 |
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- Recall: 0.5125 |
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- F1: 0.5301 |
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- Accuracy: 0.9220 |
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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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| 0.8182 | 0.59 | 500 | 0.5569 | 0.5219 | 0.2431 | 0.3317 | 0.9014 | |
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| 0.5357 | 1.19 | 1000 | 0.4980 | 0.4625 | 0.2895 | 0.3561 | 0.9024 | |
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| 0.4417 | 1.78 | 1500 | 0.4773 | 0.4029 | 0.3572 | 0.3787 | 0.9016 | |
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| 0.394 | 2.37 | 2000 | 0.4840 | 0.3697 | 0.3943 | 0.3816 | 0.8943 | |
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| 0.3534 | 2.97 | 2500 | 0.4742 | 0.3586 | 0.4437 | 0.3966 | 0.8914 | |
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| 0.3048 | 3.56 | 3000 | 0.4543 | 0.4372 | 0.4293 | 0.4332 | 0.9043 | |
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| 0.2992 | 4.15 | 3500 | 0.4846 | 0.3587 | 0.4392 | 0.3949 | 0.8907 | |
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| 0.2675 | 4.74 | 4000 | 0.4760 | 0.4100 | 0.4530 | 0.4304 | 0.9000 | |
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| 0.2454 | 5.34 | 4500 | 0.4702 | 0.4123 | 0.4407 | 0.4260 | 0.9014 | |
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| 0.2391 | 5.93 | 5000 | 0.4743 | 0.3957 | 0.4638 | 0.4270 | 0.8979 | |
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| 0.2088 | 6.52 | 5500 | 0.4778 | 0.4224 | 0.4485 | 0.4351 | 0.9038 | |
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| 0.2076 | 7.12 | 6000 | 0.5050 | 0.3736 | 0.4644 | 0.4140 | 0.8930 | |
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| 0.1946 | 7.71 | 6500 | 0.4964 | 0.4009 | 0.4599 | 0.4284 | 0.8977 | |
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| 0.1808 | 8.3 | 7000 | 0.4878 | 0.4226 | 0.4554 | 0.4384 | 0.9028 | |
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| 0.1683 | 8.9 | 7500 | 0.4947 | 0.3954 | 0.4626 | 0.4264 | 0.8976 | |
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| 0.1681 | 9.49 | 8000 | 0.4916 | 0.4081 | 0.4662 | 0.4352 | 0.9001 | |
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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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