Token Classification
Transformers
TensorBoard
Safetensors
English
modernbert
ner
cybersecurity
threat-intelligence
secureBert
Instructions to use attack-vector/SecureModernBERT-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use attack-vector/SecureModernBERT-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="attack-vector/SecureModernBERT-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("attack-vector/SecureModernBERT-NER") model = AutoModelForTokenClassification.from_pretrained("attack-vector/SecureModernBERT-NER") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72b20f1f99627fae93709c3923e6ef7d0cd18bc934c5b4e1d4dc783f7939ee93
- Size of remote file:
- 1.58 GB
- SHA256:
- 52fe43effd7557bea8056246926a2cc952953b1facb01c2c020f0b8fa5e7927f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.