Text Classification
Transformers
Safetensors
Arabic
jeb
system-one
calibrated-decisions
classification
routing
scoring
guardrails
moderation
arabic
reinforcement-learning
commercial-use
Instructions to use IJyad/jeb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IJyad/jeb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IJyad/jeb")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IJyad/jeb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer/tokenizer.json from IJyad/jeb: direct link, hf CLI and curl.
- Browser
- Download file 2.69 MB
-
https://huggingface.co/IJyad/jeb/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://IJyad/jeb/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/IJyad/jeb/resolve/main/tokenizer/tokenizer.json
2.69 MB
File too large to display, you can check the raw version instead.