--- library_name: transformers license: mit base_model: dslim/bert-base-NER tags: - generated_from_trainer metrics: - f1 model-index: - name: ner-cyber-bert results: [] --- # ner-cyber-bert This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1597 - F1: 0.6882 - Classification Report: precision recall f1-score support Indicator 0.77 0.81 0.79 270 Malware 0.70 0.79 0.74 238 Organization 0.71 0.50 0.59 133 System 0.56 0.53 0.54 236 Vulnerability 0.89 0.80 0.84 10 micro avg 0.69 0.69 0.69 887 macro avg 0.73 0.69 0.70 887 weighted avg 0.69 0.69 0.68 887 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Classification Report | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.0714 | 1.0 | 1332 | 0.0983 | 0.7004 | precision recall f1-score support Indicator 0.80 0.79 0.80 207 Malware 0.83 0.62 0.71 252 Organization 0.55 0.58 0.57 91 System 0.66 0.64 0.65 179 Vulnerability 0.83 0.56 0.67 9 micro avg 0.74 0.67 0.70 738 macro avg 0.74 0.64 0.68 738 weighted avg 0.75 0.67 0.70 738 | | 0.0829 | 2.0 | 2664 | 0.1173 | 0.7266 | precision recall f1-score support Indicator 0.78 0.75 0.77 207 Malware 0.89 0.68 0.77 252 Organization 0.67 0.43 0.52 91 System 0.68 0.73 0.70 179 Vulnerability 0.88 0.78 0.82 9 micro avg 0.77 0.68 0.73 738 macro avg 0.78 0.67 0.72 738 weighted avg 0.78 0.68 0.72 738 | | 0.0295 | 3.0 | 3996 | 0.1451 | 0.7130 | precision recall f1-score support Indicator 0.72 0.77 0.75 207 Malware 0.89 0.62 0.73 252 Organization 0.69 0.45 0.55 91 System 0.70 0.75 0.73 179 Vulnerability 0.64 0.78 0.70 9 micro avg 0.76 0.67 0.71 738 macro avg 0.73 0.67 0.69 738 weighted avg 0.77 0.67 0.71 738 | | 0.0228 | 4.0 | 5328 | 0.1244 | 0.7087 | precision recall f1-score support Indicator 0.71 0.87 0.78 207 Malware 0.91 0.62 0.74 252 Organization 0.47 0.62 0.53 91 System 0.68 0.69 0.69 179 Vulnerability 0.86 0.67 0.75 9 micro avg 0.71 0.71 0.71 738 macro avg 0.72 0.69 0.70 738 weighted avg 0.74 0.71 0.71 738 | | 0.0309 | 5.0 | 6660 | 0.1340 | 0.7458 | precision recall f1-score support Indicator 0.76 0.88 0.82 207 Malware 0.83 0.73 0.78 252 Organization 0.64 0.54 0.58 91 System 0.68 0.71 0.69 179 Vulnerability 0.70 0.78 0.74 9 micro avg 0.75 0.75 0.75 738 macro avg 0.72 0.73 0.72 738 weighted avg 0.75 0.75 0.74 738 | | 0.0068 | 6.0 | 7992 | 0.1710 | 0.7143 | precision recall f1-score support Indicator 0.73 0.86 0.79 207 Malware 0.92 0.60 0.73 252 Organization 0.57 0.51 0.53 91 System 0.66 0.71 0.68 179 Vulnerability 0.88 0.78 0.82 9 micro avg 0.74 0.69 0.71 738 macro avg 0.75 0.69 0.71 738 weighted avg 0.76 0.69 0.71 738 | | 0.0033 | 7.0 | 9324 | 0.1669 | 0.7265 | precision recall f1-score support Indicator 0.70 0.84 0.76 207 Malware 0.84 0.73 0.78 252 Organization 0.55 0.56 0.55 91 System 0.68 0.73 0.71 179 Vulnerability 0.58 0.78 0.67 9 micro avg 0.71 0.74 0.73 738 macro avg 0.67 0.73 0.69 738 weighted avg 0.72 0.74 0.73 738 | | 0.0003 | 8.0 | 10656 | 0.1820 | 0.7214 | precision recall f1-score support Indicator 0.65 0.86 0.74 207 Malware 0.88 0.67 0.76 252 Organization 0.64 0.52 0.57 91 System 0.67 0.78 0.72 179 Vulnerability 0.64 0.78 0.70 9 micro avg 0.71 0.73 0.72 738 macro avg 0.69 0.72 0.70 738 weighted avg 0.73 0.73 0.72 738 | | 0.0001 | 9.0 | 11988 | 0.1766 | 0.7270 | precision recall f1-score support Indicator 0.73 0.83 0.77 207 Malware 0.85 0.71 0.78 252 Organization 0.54 0.55 0.55 91 System 0.69 0.72 0.70 179 Vulnerability 0.54 0.78 0.64 9 micro avg 0.73 0.73 0.73 738 macro avg 0.67 0.72 0.69 738 weighted avg 0.73 0.73 0.73 738 | | 0.0018 | 10.0 | 13320 | 0.1781 | 0.7251 | precision recall f1-score support Indicator 0.71 0.86 0.78 207 Malware 0.85 0.69 0.76 252 Organization 0.54 0.55 0.54 91 System 0.67 0.74 0.70 179 Vulnerability 0.58 0.78 0.67 9 micro avg 0.72 0.73 0.73 738 macro avg 0.67 0.72 0.69 738 weighted avg 0.73 0.73 0.73 738 | ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1