Text Classification
Transformers
ONNX
Safetensors
modernbert
decision-steering
prompt-injection
indirect-prompt-injection
ai-agent-security
guardrails
llm-security
ai-safety
decision-model
jev-like
model-agnostic
erase-and-check
claim-detection
security
multilingual
mmbert
cpu
patronus
Eval Results (legacy)
text-embeddings-inference
Instructions to use patronus-studio/decision-steering-gate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patronus-studio/decision-steering-gate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="patronus-studio/decision-steering-gate")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("patronus-studio/decision-steering-gate") model = AutoModelForSequenceClassification.from_pretrained("patronus-studio/decision-steering-gate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from patronus-studio/decision-steering-gate: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/patronus-studio/decision-steering-gate/resolve/main/tokenizer.json
- Command line
-
hf download hf://patronus-studio/decision-steering-gate/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/patronus-studio/decision-steering-gate/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 17f38ebb8964f49f86aafee37c5a3f7382634805616075b6553006f98af7b026
- Size of remote file:
- 34.4 MB
- SHA256:
- 571ba459383a4799cf8d1e564abbab38b25c71470bcb4fb75c2fe957449386a7
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