Text Classification
Transformers
ONNX
English
deberta-v2
ai-safety
prompt-injection-defender
jailbreak-defender
text-embeddings-inference
Instructions to use testsavantai/prompt-injection-defender-large-v0-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use testsavantai/prompt-injection-defender-large-v0-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="testsavantai/prompt-injection-defender-large-v0-onnx")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("testsavantai/prompt-injection-defender-large-v0-onnx") model = AutoModelForSequenceClassification.from_pretrained("testsavantai/prompt-injection-defender-large-v0-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 464d936c4b827fcdeea71ca41815ce6542ccb9fb94619b952937f7be1768fe30
- Size of remote file:
- 739 MB
- SHA256:
- d2eeeb4b5bd369aa2c4a86982cb9e472bd38e944a62118616ac86f3b4f8ee0f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.