Feature Extraction
Transformers
Safetensors
English
mpnet
cybersecurity
classification
fine-tuned
text-embeddings-inference
Instructions to use selfconstruct3d/AttackGroup-MPNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selfconstruct3d/AttackGroup-MPNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="selfconstruct3d/AttackGroup-MPNET")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET") model = AutoModel.from_pretrained("selfconstruct3d/AttackGroup-MPNET") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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| Original MPNet | 0.085554 | 0.9964 |
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| MLM Fine-tuned MPNet | 0.034983 | 0.6536 |
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| ** AttackGroup-MPNET ** | 0.
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| SecBERT | 0.591303 | 0.9886 |
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| ATTACK-BERT | 0.096108 | 0.9678 |
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| SecureBERT | 0.007100 | 0.4931 |
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|-----------------------------------------------|-----------------------|----------|
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| 193 |
| Original MPNet | 0.085554 | 0.9964 |
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| 194 |
| MLM Fine-tuned MPNet | 0.034983 | 0.6536 |
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| ** AttackGroup-MPNET ** | 0.236336 | 0.9600 |
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| 196 |
| SecBERT | 0.591303 | 0.9886 |
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| 197 |
| ATTACK-BERT | 0.096108 | 0.9678 |
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| 198 |
| SecureBERT | 0.007100 | 0.4931 |
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