Text Classification
Transformers
Safetensors
Arabic
llama
arabic
guardrail
safety
jailbreak
prompt-injection
content-moderation
text-embeddings-inference
Instructions to use oddadmix/Nawah-Guard-500K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oddadmix/Nawah-Guard-500K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oddadmix/Nawah-Guard-500K")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-Guard-500K") model = AutoModelForSequenceClassification.from_pretrained("oddadmix/Nawah-Guard-500K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from oddadmix/Nawah-Guard-500K: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/oddadmix/Nawah-Guard-500K/resolve/main/training_args.bin
- Command line
-
hf download hf://oddadmix/Nawah-Guard-500K/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/oddadmix/Nawah-Guard-500K/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 7996651135f8b63aa808a9e0e21331c0c37129b9f6f27b2d7916b774352f9eb0
- Size of remote file:
- 5.2 kB
- SHA256:
- b4e376b5e5f3a24d1f52ef230b1ab033dc911df75e40112a93ca145518b3b06b
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