Instructions to use mattiaferrarini/saba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mattiaferrarini/saba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mattiaferrarini/saba")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mattiaferrarini/saba") model = AutoModelForMaskedLM.from_pretrained("mattiaferrarini/saba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d76ece259f3a4c356f11bfa18c4fdb34bbaae3d1a5538f583932922834ed195
- Size of remote file:
- 5.71 kB
- SHA256:
- fc9a26b6baa580e2e5e4d071a4c12c73e6867b0084d4e3253345b463453b0a7d
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