Instructions to use softcatala/julibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softcatala/julibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="softcatala/julibert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("softcatala/julibert") model = AutoModelForMaskedLM.from_pretrained("softcatala/julibert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3949148f5652bfabbdc5fc3ecdc5a296ef6d20f9a88dea89fee70cac230ef5a
- Size of remote file:
- 499 MB
- SHA256:
- 779dad664aad5a2485723f0140fc4c2f3e74c6ef7f9ee3e6cd69ff4aa5dbe54f
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