Instructions to use sarahwei/MITRE-v15-tactic-bert-case-based with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarahwei/MITRE-v15-tactic-bert-case-based with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sarahwei/MITRE-v15-tactic-bert-case-based")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sarahwei/MITRE-v15-tactic-bert-case-based") model = AutoModelForSequenceClassification.from_pretrained("sarahwei/MITRE-v15-tactic-bert-case-based", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add widget
Browse files
README.md
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- en
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base_model: bencyc1129/mitre-bert-base-cased
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pipeline_tag: text-classification
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---
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## MITRE-tactic-bert-case-based
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- en
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base_model: bencyc1129/mitre-bert-base-cased
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pipeline_tag: text-classification
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widget:
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- text: "An attacker performs a SQL injection."
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output:
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- label: Collection
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score: 0.0136
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- label: Command and Control
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score: 0.0757
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- label: Credential Access
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score: 0.0045
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- label: Defense Evasion
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score: 0.0094
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- label: Discovery
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score: 0.0125
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- label: Execution
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score: 0.0083
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- label: Exfiltration
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score: 0.0089
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- label: Impact
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score: 0.0067
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- label: Initial Access
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score: 0.9219
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- label: Lateral Movement
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score: 0.0175
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- label: Persistence
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score: 0.0356
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- label: Privilege Escalation
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score: 0.0325
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- label: Reconnaissance
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score: 0.0159
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- label: Resource Development
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score: 0.0081
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---
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## MITRE-tactic-bert-case-based
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