Instructions to use asyafalni/arabichar-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asyafalni/arabichar-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="asyafalni/arabichar-v3", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("asyafalni/arabichar-v3", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Arabic Handwritten Character Classification
This model is implemented using a custom CNN model, named arabichar, for the classification of handwritten arabic characters (Hijaiyah) on arabic handwritten characters dataset. It achieves the following results on the evaluation set:
- Loss: 2.4150
- Accuracy: 0.9764
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Evaluation results
- Accuracy on arabic-handwritten-charactersself-reported0.976