Text Classification
Transformers
Safetensors
English
modernbert
prompt-injection
jailbreak-detection
security
ModernBERT
ai-safety
multi-class
inference-loop
text-embeddings-inference
Instructions to use theinferenceloop/vektor-guard-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theinferenceloop/vektor-guard-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="theinferenceloop/vektor-guard-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("theinferenceloop/vektor-guard-v2") model = AutoModelForSequenceClassification.from_pretrained("theinferenceloop/vektor-guard-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 074a3d8b92b4a12da3392220d42a5af0c5a24510ffc4af68dd0e25d88f0002af
- Size of remote file:
- 1.58 GB
- SHA256:
- 9a08140d2f68667d03808cd5f419927886a2bd1090e20d72512a96c68ad6a154
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.