Text Classification
Transformers
ONNX
Safetensors
multilingual
English
modernbert
prompt-injection
jailbreak-detection
llm-security
ai-agents
guardrails
onnxruntime
mcp
text-embeddings-inference
Instructions to use usezn/Galvanize-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usezn/Galvanize-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="usezn/Galvanize-3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("usezn/Galvanize-3") model = AutoModelForSequenceClassification.from_pretrained("usezn/Galvanize-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 333 Bytes
700a2f0 | 1 2 3 4 5 6 7 | {
"probability_threshold": 0.9802263879740749,
"margin_threshold": 3.903435230255127,
"rule": "flag as prompt injection if probability >= probability_threshold (equivalently margin >= margin_threshold)",
"input_protocol": "flatten whitespace, tokenize with tokenizer.json (keeps its normalizer), keep the first 512 tokens"
}
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