Instructions to use ccaug/bert-network-attack-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ccaug/bert-network-attack-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ccaug/bert-network-attack-classifier")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ccaug/bert-network-attack-classifier") model = AutoModel.from_pretrained("ccaug/bert-network-attack-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 242b19d4b0a6f2fbb99df46df4a4f1345f0e2f9b22bcf0550b1e625d918d3778
- Size of remote file:
- 438 MB
- SHA256:
- 741b98e47d848f71a44cdd00123ec64946a698787c003f03bc032d8a5d0761a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.