Text Classification
Transformers
Safetensors
Arabic
llama
arabic
rule-checking
compliance
moderation
tiny-model
on-device
text-embeddings-inference
Instructions to use oddadmix/Nawah-RuleCheck-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oddadmix/Nawah-RuleCheck-1M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oddadmix/Nawah-RuleCheck-1M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-RuleCheck-1M") model = AutoModelForSequenceClassification.from_pretrained("oddadmix/Nawah-RuleCheck-1M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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ship LADDER.json for reproducibility
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Card regenerated from eval_all.py - one harness, all four rungs
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ship LADDER.json for reproducibility
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ship ood_cases.json for reproducibility
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ship cf_cases.json for reproducibility
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Card regenerated from eval_all.py
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Arabic rule-checking classifier — size-ladder rung
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initial commit
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