Instructions to use M47Labs/binary_classification_arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M47Labs/binary_classification_arabic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="M47Labs/binary_classification_arabic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("M47Labs/binary_classification_arabic") model = AutoModelForSequenceClassification.from_pretrained("M47Labs/binary_classification_arabic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from M47Labs/binary_classification_arabic: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/M47Labs/binary_classification_arabic/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://M47Labs/binary_classification_arabic/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/M47Labs/binary_classification_arabic/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 7fa42fb7cce5f0c847234120486204249a6e1cf81171b222e16d900de80b8d81
- Size of remote file:
- 443 MB
- SHA256:
- 7f10326c632cfde7126c5edab5d059b1e1dae499c0a8933c007e8adc0a01be49
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