Instructions to use Matankl/arabert-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matankl/arabert-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Matankl/arabert-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Matankl/arabert-classification") model = AutoModelForSequenceClassification.from_pretrained("Matankl/arabert-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Matankl/arabert-classification: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/Matankl/arabert-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Matankl/arabert-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Matankl/arabert-classification/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- ea7a4d14f1618dead38e9e73dddc52432c3c087355dcd726fc68251c0ac61b5a
- Size of remote file:
- 541 MB
- SHA256:
- d8f03abc840acfb32efd1be0c49534960ca5a2b987582f686d570f9004da24bc
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