|
--- |
|
license: bigscience-openrail-m |
|
base_model: ehsanaghaei/SecureBERT |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: Cyber-ThreaD/SecureBERT-DNRTI |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# Cyber-ThreaD/SecureBERT-DNRTI |
|
|
|
This model is a fine-tuned version of [ehsanaghaei/SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) on the [DNRTI](https://github.com/SCreaMxp/DNRTI-A-Large-scale-Dataset-for-Named-Entity-Recognition-in-Threat-Intelligence) dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2427 |
|
- Precision: 0.7694 |
|
- Recall: 0.7854 |
|
- F1: 0.7773 |
|
- Accuracy: 0.9382 |
|
|
|
It achieves the following results on the prediction set: |
|
- Precision: 0.8346 |
|
- Recall: 0.8403 |
|
- F1: 0.8374 |
|
- Accuracy: 0.9554 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 2e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.7821 | 0.76 | 500 | 0.4215 | 0.5219 | 0.5919 | 0.5547 | 0.8745 | |
|
| 0.3559 | 1.52 | 1000 | 0.3152 | 0.6272 | 0.6587 | 0.6426 | 0.9008 | |
|
| 0.2807 | 2.28 | 1500 | 0.2952 | 0.6445 | 0.7232 | 0.6816 | 0.9084 | |
|
| 0.2272 | 3.04 | 2000 | 0.2793 | 0.6682 | 0.7513 | 0.7073 | 0.9161 | |
|
| 0.1837 | 3.81 | 2500 | 0.2489 | 0.7151 | 0.7526 | 0.7334 | 0.9258 | |
|
| 0.1497 | 4.57 | 3000 | 0.2511 | 0.7254 | 0.7826 | 0.7529 | 0.9286 | |
|
| 0.1371 | 5.33 | 3500 | 0.2496 | 0.7425 | 0.7757 | 0.7587 | 0.9331 | |
|
| 0.1135 | 6.09 | 4000 | 0.2554 | 0.7289 | 0.8075 | 0.7662 | 0.9325 | |
|
| 0.1018 | 6.85 | 4500 | 0.2427 | 0.7694 | 0.7854 | 0.7773 | 0.9382 | |
|
| 0.0899 | 7.61 | 5000 | 0.2516 | 0.7583 | 0.8167 | 0.7864 | 0.9378 | |
|
| 0.0809 | 8.37 | 5500 | 0.2459 | 0.7717 | 0.8176 | 0.7940 | 0.9406 | |
|
| 0.0763 | 9.13 | 6000 | 0.2553 | 0.7518 | 0.8217 | 0.7852 | 0.9392 | |
|
| 0.0687 | 9.89 | 6500 | 0.2534 | 0.7621 | 0.8204 | 0.7902 | 0.9407 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.0.dev0 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |
|
|
|
|
|
### Citing & Authors |
|
|
|
If you use the model kindly cite the following work |
|
|
|
``` |
|
@inproceedings{deka2024attacker, |
|
title={AttackER: Towards Enhancing Cyber-Attack Attribution with a Named Entity Recognition Dataset}, |
|
author={Deka, Pritam and Rajapaksha, Sampath and Rani, Ruby and Almutairi, Amirah and Karafili, Erisa}, |
|
booktitle={International Conference on Web Information Systems Engineering}, |
|
pages={255--270}, |
|
year={2024}, |
|
organization={Springer} |
|
} |
|
|
|
``` |
|
|
