Text Classification
Transformers
Safetensors
Arabic
llama
arabic
rule-checking
compliance
moderation
tiny-model
on-device
text-embeddings-inference
Instructions to use oddadmix/Nawah-RuleCheck-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oddadmix/Nawah-RuleCheck-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oddadmix/Nawah-RuleCheck-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-RuleCheck-v2") model = AutoModelForSequenceClassification.from_pretrained("oddadmix/Nawah-RuleCheck-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed01fb711d6fd697679c240cc5c48dfabe5e54392e32d9a4bcb746c7aac3898e
- Size of remote file:
- 5.2 kB
- SHA256:
- 1cbbd0d9d474a7534a0a3ea0c2ecfb47180100c1292315d9e7d8029c1eda3799
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