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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: anonymouspd/CyBERT-DNRTI |
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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-DNRTI |
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This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on the [DNRTI](https://github.com/SCreaMxp/DNRTI-A-Large-scale-Dataset-for-Named-Entity-Recognition-in-Threat-Intelligence) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3378 |
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- Precision: 0.5628 |
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- Recall: 0.6439 |
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- F1: 0.6006 |
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- Accuracy: 0.9077 |
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It achieves the following results on the prediction set: |
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- Loss: 0.2841 |
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- Precision: 0.6301 |
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- Recall: 0.6926 |
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- F1: 0.6599 |
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- Accuracy: 0.9201 |
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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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| 0.8529 | 0.76 | 500 | 0.5937 | 0.4470 | 0.3593 | 0.3984 | 0.8508 | |
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| 0.5566 | 1.52 | 1000 | 0.5027 | 0.4669 | 0.4196 | 0.4420 | 0.8636 | |
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| 0.4678 | 2.28 | 1500 | 0.4671 | 0.4706 | 0.4832 | 0.4768 | 0.8694 | |
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| 0.4038 | 3.04 | 2000 | 0.4320 | 0.4629 | 0.5371 | 0.4972 | 0.8739 | |
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| 0.3572 | 3.81 | 2500 | 0.4002 | 0.5134 | 0.5394 | 0.5261 | 0.8858 | |
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| 0.3167 | 4.57 | 3000 | 0.4047 | 0.4691 | 0.6094 | 0.5302 | 0.8826 | |
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| 0.2987 | 5.33 | 3500 | 0.3761 | 0.5158 | 0.5854 | 0.5484 | 0.8948 | |
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| 0.2706 | 6.09 | 4000 | 0.3558 | 0.5362 | 0.6066 | 0.5693 | 0.9001 | |
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| 0.2461 | 6.85 | 4500 | 0.3493 | 0.5511 | 0.5735 | 0.5621 | 0.9028 | |
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| 0.2311 | 7.61 | 5000 | 0.3526 | 0.5334 | 0.6518 | 0.5867 | 0.9024 | |
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| 0.2171 | 8.37 | 5500 | 0.3418 | 0.5586 | 0.6407 | 0.5969 | 0.9071 | |
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| 0.2062 | 9.13 | 6000 | 0.3378 | 0.5628 | 0.6439 | 0.6006 | 0.9077 | |
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| 0.1972 | 9.89 | 6500 | 0.3384 | 0.5648 | 0.6527 | 0.6056 | 0.9087 | |
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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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