Instructions to use AfnanTS/AraBERT-EngData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AfnanTS/AraBERT-EngData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AfnanTS/AraBERT-EngData")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AfnanTS/AraBERT-EngData") model = AutoModelForMaskedLM.from_pretrained("AfnanTS/AraBERT-EngData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AfnanTS/AraBERT-EngData: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/AfnanTS/AraBERT-EngData/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AfnanTS/AraBERT-EngData/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AfnanTS/AraBERT-EngData/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 73035b4d7cee1866d21a448b82c7f16f9a128d932d8c23216d1d6b9b12560a6b
- Size of remote file:
- 541 MB
- SHA256:
- f8a932a0e58d5c597740e19d9e690f47e83764aa7293444d8f166e39541249d0
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