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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SI2M-Lab/DarijaBERT") model = AutoModelForMaskedLM.from_pretrained("SI2M-Lab/DarijaBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5654e703da36daddc2e8987027a585ccc02e7e955a1d8c4883e272443e976ad5
- Size of remote file:
- 836 MB
- SHA256:
- 3247fdc84665eec9a479ce3fceb2f52464fdc88633ac8091ecd9e8fc2c74695d
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