Text Classification
Transformers
ONNX
Safetensors
English
deberta-v2
prompt-injection
injection
security
llm-security
Generated from Trainer
text-embeddings-inference
Instructions to use protectai/deberta-v3-base-prompt-injection-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use protectai/deberta-v3-base-prompt-injection-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="protectai/deberta-v3-base-prompt-injection-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("protectai/deberta-v3-base-prompt-injection-v2") model = AutoModelForSequenceClassification.from_pretrained("protectai/deberta-v3-base-prompt-injection-v2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from protectai/deberta-v3-base-prompt-injection-v2: direct link, hf CLI and curl.
- Browser
- Download file 23 Bytes
-
https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2/resolve/main/added_tokens.json
- Command line
-
hf download hf://protectai/deberta-v3-base-prompt-injection-v2/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2/resolve/main/added_tokens.json
23 Bytes
| { | |
| "[MASK]": 128000 | |
| } | |