Instructions to use Cyber-ThreaD/SecureBERT-AttackER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cyber-ThreaD/SecureBERT-AttackER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Cyber-ThreaD/SecureBERT-AttackER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Cyber-ThreaD/SecureBERT-AttackER") model = AutoModelForTokenClassification.from_pretrained("Cyber-ThreaD/SecureBERT-AttackER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 999fd75ad4c3ea11d22e367ac725e64f28639007cab7bb03c4112ea8f4db68ab
- Size of remote file:
- 4.66 kB
- SHA256:
- 54dbc352a92b7aedc19bf07ab796de02e755a77b802fad540649dcf5026c98b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.