Text Classification
Transformers
Safetensors
Arabic
llama
arabic
rule-checking
compliance
moderation
tiny-model
on-device
text-embeddings-inference
Instructions to use oddadmix/Nawah-RuleCheck-500K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oddadmix/Nawah-RuleCheck-500K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oddadmix/Nawah-RuleCheck-500K")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-RuleCheck-500K") model = AutoModelForSequenceClassification.from_pretrained("oddadmix/Nawah-RuleCheck-500K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Nawah-RuleCheck-500K: 2 MB Arabic rule checker, 0.978 on unseen rule wording
Verified signature0.52M params, within 0.2 points of the 51.8M model on unseen phrasings. LR 6e-3 / 15 epochs - the recipe matters far more at this size than the 6-point gap under the larger model's hyperparameters suggested.01913e7