Text Classification
Transformers
Safetensors
Arabic
llama
arabic
rule-checking
compliance
moderation
tiny-model
on-device
text-embeddings-inference
Instructions to use oddadmix/Nawah-RuleCheck-5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oddadmix/Nawah-RuleCheck-5M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oddadmix/Nawah-RuleCheck-5M")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-RuleCheck-5M") model = AutoModelForSequenceClassification.from_pretrained("oddadmix/Nawah-RuleCheck-5M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62896d02846e275343b1d5022871dc2825cdf997e12d7527665fe7286d0bd83d
- Size of remote file:
- 5.2 kB
- SHA256:
- b928245070b00914149cfb5cd4a415a1161fcb2ee1738c318e63c2a2a9249064
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