Text Classification
Transformers
Safetensors
Arabic
deberta-v2
arabic
llm
security
saftey
text-embeddings-inference
Instructions to use NAMAA-Space/Ara-Prompt-Guard_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NAMAA-Space/Ara-Prompt-Guard_V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NAMAA-Space/Ara-Prompt-Guard_V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NAMAA-Space/Ara-Prompt-Guard_V1") model = AutoModelForSequenceClassification.from_pretrained("NAMAA-Space/Ara-Prompt-Guard_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75860198167e31083997d62d5a31c6a0a3f01160926bd500c8aca764579880e3
- Size of remote file:
- 16 MB
- SHA256:
- 7fa35d64acf61c7e0e64846d480e102e3943f5b7cbde0b7603bb17db8c65511f
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