Instructions to use Sifal/dzarabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sifal/dzarabert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Sifal/dzarabert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Sifal/dzarabert") model = AutoModelForMaskedLM.from_pretrained("Sifal/dzarabert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fa6cb2dbf308726638adbca72a476a06c0ae79ff64e2cf215ef5493efb4f688
- Size of remote file:
- 451 MB
- SHA256:
- e1b6cd55782905e044af7123105750e14a1d71005bbf92c75b8880deaee82b4d
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