Instructions to use akhooli/ModernBERT-ar-base-65m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akhooli/ModernBERT-ar-base-65m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="akhooli/ModernBERT-ar-base-65m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("akhooli/ModernBERT-ar-base-65m") model = AutoModelForMaskedLM.from_pretrained("akhooli/ModernBERT-ar-base-65m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from akhooli/ModernBERT-ar-base-65m: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/akhooli/ModernBERT-ar-base-65m/resolve/main/training_args.bin
- Command line
-
hf download hf://akhooli/ModernBERT-ar-base-65m/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/akhooli/ModernBERT-ar-base-65m/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 57adf31881925027f7640ca09cc74e70dff14fa24c9b69b9594e92eeace3b0b1
- Size of remote file:
- 5.3 kB
- SHA256:
- 9d70c9e4991a9e030afc2b89d7575023a0f3ceb0e5b4bf80be08f89fc80bd0a8
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