Text Classification
Transformers
Safetensors
deberta-v2
prompt-injection
injection
security
llm-security
Generated from Trainer
text-embeddings-inference
Instructions to use proventra/mdeberta-v3-base-prompt-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use proventra/mdeberta-v3-base-prompt-injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="proventra/mdeberta-v3-base-prompt-injection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("proventra/mdeberta-v3-base-prompt-injection") model = AutoModelForSequenceClassification.from_pretrained("proventra/mdeberta-v3-base-prompt-injection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bf25eb20a84a9efdb04577b865aef30d00f374f5df79f47900c77fd68148940
- Size of remote file:
- 5.3 kB
- SHA256:
- 74b3f06e30cd83ba43ceee856603ecd0d9301607821e381db7092ce5f28b8b6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.