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