Instructions to use SI2M-Lab/DarijaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SI2M-Lab/DarijaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SI2M-Lab/DarijaBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SI2M-Lab/DarijaBERT") model = AutoModelForMaskedLM.from_pretrained("SI2M-Lab/DarijaBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from SI2M-Lab/DarijaBERT: direct link, hf CLI and curl.
- Browser
- Download file 836 MB
-
https://huggingface.co/SI2M-Lab/DarijaBERT/resolve/main/model.safetensors
- Command line
-
hf download hf://SI2M-Lab/DarijaBERT/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/SI2M-Lab/DarijaBERT/resolve/main/model.safetensors
836 MB
- Xet hash:
- c3f2539c13ec70904eaac2b95c724e52365504be19830883a1fe8dc27449aa56
- Size of remote file:
- 836 MB
- SHA256:
- 6f1b24e51989b3c144b7a2e40d73b6ac889e2bbe2fe8fc59c12d637a2dd0679f
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