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")# pip install -U transformers accelerate # 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
Download training_meta.json from ctokx/cti-attack-mapper-modernbert: direct link, hf CLI and curl.
- Browser
- Download file 357 Bytes
-
https://huggingface.co/ctokx/cti-attack-mapper-modernbert/resolve/main/training_meta.json
- Command line
-
hf download hf://ctokx/cti-attack-mapper-modernbert/training_meta.json
-
curl -L -o training_meta.json https://huggingface.co/ctokx/cti-attack-mapper-modernbert/resolve/main/training_meta.json
357 Bytes
| { | |
| "base_model": "answerdotai/ModernBERT-base", | |
| "split_scheme": "document", | |
| "epochs": 6, | |
| "best_epoch": 5, | |
| "best_dev_macro_f1": 0.4139, | |
| "best_dev_global_threshold": 0.45, | |
| "max_length": 256, | |
| "batch_size": 16, | |
| "grad_accum": 2, | |
| "learning_rate": 3e-05, | |
| "seed": 20260802, | |
| "n_train": 11668, | |
| "n_dev": 3134, | |
| "n_labels": 49 | |
| } |