Text Classification
Transformers
Safetensors
English
modernbert
cyber-threat-intelligence
mitre-attack
multi-label-classification
defensive-security
blue-team
threat-intelligence
text-embeddings-inference
Instructions to use ctokx/cti-attack-mapper-modernbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctokx/cti-attack-mapper-modernbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ctokx/cti-attack-mapper-modernbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ctokx/cti-attack-mapper-modernbert") model = AutoModelForSequenceClassification.from_pretrained("ctokx/cti-attack-mapper-modernbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """Build the cleaned dataset with both split schemes. | |
| python scripts/01_build_dataset.py | |
| """ | |
| import sys | |
| from pathlib import Path | |
| sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) | |
| from cti_attack import data # noqa: E402 | |
| def main() -> None: | |
| records, labels, stats = data.build(verbose=True) | |
| print() | |
| data.write(records, labels, stats) | |
| print(f"\n{len(labels)} techniques retained") | |
| if __name__ == "__main__": | |
| main() | |