Instructions to use Kamel/AraSDG_Binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kamel/AraSDG_Binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kamel/AraSDG_Binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kamel/AraSDG_Binary") model = AutoModelForSequenceClassification.from_pretrained("Kamel/AraSDG_Binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb9b89d7cf9348dc4a20d4d2e26b623839c87daa2f1555ff9a9e02424a185ff4
- Size of remote file:
- 5.05 kB
- SHA256:
- 2cbf44408d5778eed7a77b94e6aa3a236b5cd2a675a7c65e5f4f19ff25d5fca2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.